Multifactorial optimization system and method

ABSTRACT

A method for providing unequal allocation of rights among agents while operating according to fair principles, comprising assigning a hierarchal rank to each agent; providing a synthetic economic value to a first set of agents at the a high level of the hierarchy; allocating portions of the synthetic economic value by the first set of agents to a second set of agents at respectively different hierarchal rank than the first set of agents; and conducting an auction amongst agents using the synthetic economic value as the currency. A method for allocation among agents, comprising assigning a wealth generation function for generating future wealth to each of a plurality of agents, communicating subjective market information between agents, and transferring wealth generated by the secure wealth generation function between agents in consideration of a market transaction. The method may further comprise the step of transferring at least a portion of the wealth generation function between agents.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a Divisional of U.S. patent application Ser.No. 12/089,277, filed Apr. 4, 2008, issued as U.S. Pat. No. 8,144,619,issued Mar. 27, 2012, which is a U.S. National Stage application under35 U.S.C. §371 of PCT/US06/38759, filed Oct. 3, 2006, and claims benefitof priority from U.S. patent application Ser. No. 11/467,931 filed Aug.29, 2006 and U.S. Provisional Patent Application No. 60/723,339, filedOct. 4, 2005, each of which is expressly incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates to the field of multifactorial economicoptimization, and more generally to optimization of communities ofelements having conflicting requirements and overlapping resources.

BACKGROUND OF THE INVENTION

In modern retail transactions, predetermined price transactions arecommon, with market transactions, i.e., commerce conducted in a settingwhich allows the transaction price to float based on the respectivevaluation allocated by the buyer(s) and seller(s), often left tospecialized fields. While interpersonal negotiation is often used to seta transfer price, this price is often different from a transfer pricethat might result from a best-efforts attempt at establishing a marketprice. Assuming that the market price is optimal, it is thereforeassumed that alternatives are sub-optimal. Therefore, the establishmentof a market price is desirable over simple negotiations.

One particular problem with market-based commerce is that both selleroptimization and market efficiency depend on the fact thatrepresentative participants of a preselected class are invited toparticipate, and are able to promptly communicate, on a relevanttimescale, in order to accurately value the goods or services and makean offer. Thus, in traditional market-based system, all participants arein the same room, or connected by a high quality (low latency, lowerror) telecommunications link. Alternately, the market valuationprocess is prolonged over an extended period, allowing non-real timecommunications of market information and bids. Thus, attempts atascertaining a market price for non-commodity goods can be subject tosubstantial inefficiencies, which reduce any potential gains by marketpricing. Further, while market pricing might be considered “fair”, italso imposes an element of risk, reducing the ability of parties topredict future pricing and revenues. Addressing this risk may alsoimprove efficiency of a market-based system, that is, increase theoverall surplus in the market.

When a single party seeks to sell goods to the highest valuedpurchaser(s), to establish a market price, the rules of conducttypically define an auction. Typically, known auctions provide anascending price or descending price over time, with bidders makingoffers or ceasing to make offers, in the descending price or ascendingprice models, respectively, to define the market price. Afterdetermining the winner of the auction, typically a bidder whoestablishes a largest economic surplus, the pricing rules define thepayment, which may be in accordance with a uniform price auction,wherein all successful bidders pay the lowest successful bid, a secondprice auction wherein the winning bidder pays the amount bid by the nexthighest bidder, and pay-what-you-bid (first price) auctions. Thepay-what-you-bid auction is also known as a discriminative auction whilethe uniform price auction is known as a non-discriminative auction. In asecond-price auction, also known as a Vickrey auction, the policy seeksto create a disincentive for speculation and to encourage bidders tosubmit bids reflecting their true value for the good, rather than“shaving” the bid to achieve a lower cost. In the uniform price andsecond price schemes, the bidder is encourages to disclose the actualprivate value to the bidder of the good or service, since at any pricebelow this amount, there is an excess gain to the buyer, whereas bywithholding this amount the bid may be unsuccessful, resulting in a lossof the presumably desirable opportunity. In the pay-what-you-bidauction, on the other hand, the buyer need not disclose the maximumprivate valuation, and those bidders with lower risk tolerance will bidhigher prices. See, ww.isoc.org/inet98/proceedings/3b/3b_(—)3.html;www.ibm.com/iac/reports-technical/reports-bus-neg-internet.html.

Two common types of auction are the English auction, which sells asingle good to the highest bidder in an ascending price auction, and theDutch auction, in which multiple units are available for sale, and inwhich a starting price is selected by the auctioneer, which issuccessively reduced, until the supply is exhausted by bidders (or theminimum price/final time is reached), with the buyer(s) paying thelowest successful bid. The term Dutch auction is also applied to a typeof sealed bid auction. In a multi-unit live Dutch auction, eachparticipant is provided with the current price, the quantity on hand andthe time remaining in the auction. This type of auction, typically takesplace over a very short period of time and there is a flurry of activityin the last portion of the auction process. The actual auctionterminates when there is no more product to be sold or the time periodexpires.

In selecting the optimal type of auction, a number of factors areconsidered. In order to sell large quantities of a perishable commodityin a short period of time, the descending price auctions are oftenpreferred. For example, the produce and flower markets in Hollandroutinely use the Dutch auction (hence the derivation of the name),while the U.S. Government uses this form to sell its financialinstruments. The format of a traditional Dutch auction encourages earlybidders to bid up to their “private value”, hoping to pay some pricebelow the “private value”. In making a bid, the “private value” becomesknown, helping to establish a published market value and demand curvefor the goods, thus allowing both buyers and sellers to definestrategies for future auctions.

In an auction, typically a seller retains an auctioneer to conduct anauction with multiple buyers. (In a reverse auction, a buyer solicitsthe lowest price from multiple competing vendors for a desiredpurchase). Since the seller retains the auctioneer, the selleressentially defines the rules of the auction. These rules are typicallydefined to maximize the revenues or profit to the seller, whileproviding an inviting forum to encourage a maximum number of high valuedbuyers. If the rules discourage high valuations of the goods orservices, or discourage participation by an important set of potentialbidders, then the rules are not optimum. Rules may also be imposed todiscourage bidders who are unlikely to submit winning bids fromconsuming resources. A rule may also be imposed to account for thevaluation of the good or service applied by the seller, in the form of areserve price. It is noted that these rules typically seek to allocateto the seller a portion of the economic benefit that would normallyinure to the buyer (in a perfectly efficient auction), creating aneconomic inefficiency. However, since the auction is to benefit theseller, not society as a whole, this potential inefficiency istolerated. An optimum auction thus seeks to produce a maximum profit (ornet revenues) for the seller. An efficient auction, on the other hand,maximizes the sum of he utilities for the buyer and seller. It remains asubject of academic debate as to which auction rules are most optimum ingiven circumstances; however, in practice, simplicity of implementationmay be a paramount concern, and simple auctions may result in highestrevenues; complex auctions, while theoretically more optimal, maydiscourage bidders from participating or from applying their true andfull private valuation in the auction process.

Typically, the rules of the auction are predefined and invariant.Further, for a number of reasons, auctions typically apply the samerules to all bidders, even though, with a priori knowledge of theprivate values assigned by each bidder to the goods, or a prediction ofthe private value, an optimization rule may be applied to extract thefull value assigned by each bidder, while selling above the seller'sreserve.

In a known ascending price auction, each participant must be made awareof the status of the auction, e.g., open, closed, and thecontemporaneous price. A bid is indicated by the identification of thebidder at the contemporaneous price, or occasionally at any price abovethe minimum bid increment plus the previous price. The bids areasynchronous, and therefore each bidder must be immediately informed ofthe particulars of each bid by other bidders.

In a known descending price auction, the process traditionally entails acommon clock, which corresponds to a decrementing price at eachdecrement interval, with an ending time (and price). Therefore, onceeach participant is made aware of the auction parameters, e.g., startingprice, price decrement, ending price/time, before the start of theauction, the only information that must be transmitted is auction status(e.g., inventory remaining).

As stated above, an auction is traditionally considered an efficientmanner of liquidating goods at a market price. The theory of an auctionis that either the buyer will not resell, and thus has an internal orprivate valuation of the goods regardless of other's perceived values,or that the winner will resell, either to gain economic efficiency or asa part of the buyer's regular business. In the later case, it is ageneral presumption that the resale buyers are not in attendance at theauction or are otherwise precluded from bidding, and therefore that,after the auction, there will remain demand for the goods at a price inexcess of the price paid during the auction. Extinction of this residualdemand results in the so-called “winner's curse”, in which the buyer canmake no profit from the transaction during the auction. Since thisdetracts from the value of the auction as a means of conductingprofitable commerce, it is of concern to both buyer and seller.

Research into auction theory (game theory) shows that in an auction, thegoal of the seller is to optimize the auction by allocating the goodsinefficiently, if possible, and thus to appropriate to himself an excessgain. This inefficiency manifests itself by either withholding goodsfrom the market or placing the goods in the wrong hands. In order toassure for the seller a maximum gain from a misallocation of the goods,restrictions on resale are imposed; otherwise, post auction trading willtend to undue the misallocation, and the anticipation of this tradingwill tend to control the auction pricing. The misallocations of goodsimposed by the seller through restrictions allow the seller to achievegreater revenues than if free resale were permitted. It is believed thatin an auction followed by perfect resale, that any mis-assignment of thegoods lowers the seller's revenues below the optimum and likewise, in anauction market followed by perfect resale, it is optimal for the sellerto allocate the goods to those with the highest value. Therefore, ifpost-auction trading is permitted, the seller will not benefit fromthese later gains, and the seller will obtain sub optimal revenues.

These studies, however, typically do not consider transaction costs andinternal inefficiencies of the resellers, as well as the possibility ofmultiple classes of purchasers, or even multiple channels ofdistribution, which may be subject to varying controls or restrictions,and thus in a real market, such theoretical optimal allocation isunlikely. In fact, in real markets the transaction costs involved intransfer of ownership are often critical in determining a method of saleand distribution of goods. For example, it is the efficiency of salethat motivates the auction in the first place. Yet, the auction processitself may consume a substantial margin, for example 1-15% of thetransaction value. To presume, even without externally imposedrestrictions on resale, that all of the efficiencies of the market maybe extracted by free reallocation, ignores that the motivation of thebuyer is a profitable transaction, and the buyer may have fixed andvariable costs on the order of magnitude of the margin. Thus, there aresubstantial opportunities for the seller to gain enhanced revenues bydefining rules of the auction, strategically allocating inventory amountand setting reserve pricing.

Therefore, perfect resale is but a fiction created in auction (game)theory. Given this deviation from the ideal presumptions, auction theorymay be interpreted to provide the seller with a motivation tomisallocate or withhold based on the deviation of practice from theory,likely based on the respective transaction costs, seller's utility ofthe goods, and other factors not considered by the simple analyses.

In many instances, psychology plays an important role in the conduct ofthe auction. In a live auction, bidders can see each other, and judgethe tempo of the auction. In addition, multiple auctions are oftenconducted sequentially, so that each bidder can begin to understand theother bidder's patterns, including hesitation, bluffing, facial gesturesor mannerisms. Thus, bidders often prefer live auctions to remote orautomated auctions if the bidding is to be conducted strategically.

Internet auctions are quite different from live auctions with respect topsychological factors. Live auctions are often monitored closely bybidders, who strategically make bids, based not only on the “value” ofthe goods, but also on an assessment of the competition, timing,psychology, and progress of the auction. It is for this reason thatso-called proxy bidding, wherein the bidder creates a preprogrammed“strategy”, usually limited to a maximum price, are disfavored as ameans to minimize purchase price, and offered as a service byauctioneers who stand to make a profit based on the transaction price. Amaximum price proxy bidding system is somewhat inefficient, in thatother bidders may test the proxy, seeking to increase the bid price,without actually intending to purchase, or contrarily, after testing theproxy, a bidder might give up, even below a price he might have beenwilling to pay. Thus, the proxy imposes inefficiency in the system thateffectively increases the transaction cost.

In order to address a flurry of activity that often occurs at the end ofan auction, an auction may be held open until no further bids arecleared for a period of time, even if advertised to end at a certaintime. This is common to both live and automated auctions. However, thislack of determinism may upset coordinated schedules, thus impairingefficient business use of the auction system.

Game Theory

Use of Game Theory to control arbitration of ad hoc networks is wellknown. F. P. Kelly, A. Maulloo, and D. Tan. Rate control incommunication networks: shadow prices, proportional fairness andstability. Journal of the Operational Research Society, 49, 1998.citeseer.ist.psu.edu/kelly98rate.html; J. MacKie-Mason and H. Varian.Pricing congestible network resources. IEEE Journal on Selected Areas inCommunications, 13(7):1141-1149, 1995. Some prior studies have focusedon the incremental cost to each node for participation in the network,without addressing the opportunity cost of a node foregoing control overthe communication medium. Courcoubetis, C., Siris, V. A. and Stamoulis,G. D. Integration of pricing and flow control for available bit rateservices in ATM networks. In Proceedings IEEE Globecom '96, pp. 644-648.London, UK. citeseer.ist.psu.edu/courcoubetis96integration.html.

A game theoretic approach addresses the situation where the operation ofan agent which has freedom of choice, allowing optimization on a highlevel, considering the possibility of alternatives to a well designedsystem. According to game theory, the best way to ensure that a systemretains compliant agents is to provide the greatest anticipated benefit,at the least anticipated cost, compared to the alternates.

Game Theory provides a basis for understanding the actions of Ad hocnetwork nodes. A multihop ad hoc network requires a communication to bepassed through a disinterested node. The disinterested node incurs somecost, thus leading to a disincentive to cooperate. Meanwhile, bystandernodes must defer their own communications in order to avoidinterference, especially in highly loaded networks. By understanding thedecision analysis of the various nodes in a network, it is possible tooptimize a system which, in accordance with game theory, providesbenefits or incentives, to promote network reliability and stability.The incentive, in economic form, may be charged to those benefiting fromthe communication, and is preferably related to the value of the benefitreceived. The proposed network optimization scheme employs a modifiedcombinatorial (VCG) auction, which optimally compensates those involvedin the communication, with the benefiting party paying the secondhighest bid price (second price). The surplus between the second priceand VCG price is distributed among those who defer to the winning bidderaccording to respective bid value. Equilibrium usage and headroom may beinfluenced by deviating from a zero-sum condition. The mechanism seeksto define fairness in terms of market value, providing probableparticipation benefit for all nodes, leading to network stability.

An ad hoc network is a wireless network which does not require fixedinfrastructure or centralized control. The terminals in the networkcooperate and communicate with each other, in a self organizing network.In a multihop network, communications can extend beyond the scope of asingle node, employing neighboring nodes to forward messages to theirdestination. In a mobile ad hoc network, constraints are not placed onthe mobility of nodes, that is, they can relocate within a time scalewhich is short with respect to the communications, thus requiringconsideration of dynamic changes in network architecture.

Ad hoc networks pose control issues with respect to contention, routingand information conveyance. There are typically tradeoffs involvingequipment size, cost and complexity, protocol complexity, throughputefficiency, energy consumption, and “fairness” of access arbitration.Other factors may also come into play. L. Buttyan and J.-P. Hubaux.Rational exchange—a formal model based on game theory. In Proceedings ofthe 2nd International Workshop on Electronic Commerce (WELCOM), November2001. citeseer.ist.psu.edu/an01rational.html; P. Michiardi and R. Molva.Game theoretic analysis of security in mobile ad hoc networks. TechnicalReport RR-02-070, Institut Eurécom, 2002; P. Michiardi and R. Molva. Agame theoretical approach to evaluate cooperation enforcement mechanismsin mobile ad hoc networks. In Proceedings of WiOpt '03, March 2003;Michiardi, P., Molva, R.: Making greed work in mobile ad hoc networks.Technical report, Institut Eurecom (2002)citeseer.ist.psu.edu/michiardi02making.html; S. Shenker. Making greedwork in networks: A game-theoretic analysis of switch servicedisciplines. IEEE/ACM Transactions on Networking, 3(6):819-831, December1995; A. B. MacKenzie and S. B. Wicker. Selfish users in aloha: Agame-theoretic approach. In Vehicular Technology Conference, 2001. VTC2001 Fall. IEEE VTS 54th, volume 3, October 2001; J. Crowcroft, R.Gibbens, F. Kelly, and S. Östring. Modelling incentives forcollaboration in mobile ad hoc networks. In Proceedings of WiOpt '03,2003.

Game theory studies the interactions of multiple independent decisionmakers, each seeking to fulfill their own objectives. Game theoryencompasses, for example, auction theory and strategic decision-making.By providing appropriate incentives, a group of independent actors maybe persuaded, according to self-interest, to act toward the benefit ofthe group. That is, the selfish individual interests are aligned withthe community interests. In this way, the community will be bothefficient and the network of actors stable and predictable. Of course,any systems wherein the “incentives” impose too high a cost, themselvesencourage circumvention. In this case, game theory also addresses thisissue.

In computer networks, issues arise as the demand for communicationsbandwidth approaches the theoretical limit. Under such circumstances,the behavior of nodes will affect how close to the theoretical limit thesystem comes, and also which communications are permitted. The wellknown collision sense, multiple access (CSMA) protocol allows each nodeto request access to the network, essentially without cost or penalty,and regardless of the importance of the communication. While theprotocol incurs relatively low overhead and may provide fullydecentralized control, under congested network conditions, the systemmay exhibit instability, that is, a decline in throughput as demandincreases, resulting in ever increasing demand on the system resourcesand decreasing throughput. Durga P. Satapathy and Jon M. Peha,Performance of Unlicensed Devices With a Spectrum Etiquette,”Proceedings of IEEE Globecom, November 1997, pp. 414-418.citeseer.ist.psu.edu/satapathy97 performance.html. According to gametheory, the deficit of the CSMA protocol is that it is a dominantstrategy to be selfish and hog resources, regardless of the cost tosociety, resulting in “the tragedy of the commons.” Garrett Hardin. TheTragedy of the Commons. Science, 162:1243-1248, 1968. AlternateLocation: dieoff.com/page95.htm.

In an ad hoc network used for conveying real-time information, as mightbe the case in a telematics system, there are potentially unlimited datacommunication requirements (e.g., video data), and network congestion isalmost guaranteed. Therefore, using a CSMA protocol as the paradigm forbasic information conveyance is destined for failure, unless there is adisincentive to network use. (In power constrained circumstances, thiscost may itself provide such a disincentive). On the other hand, asystem which provides more graceful degradation under high load,sensitivity to the importance of information to be communicated, andefficient utilization of the communications medium would appear moreoptimal.

One way to impose a cost which varies in dependence on the societalvalue of the good or service is to conduct an auction, which is amechanism to determine the market value of the good or service, at leastbetween the auction participants. Walsh, W. and M. Wellman (1998). Amarket protocol for decentralized task allocation, in “Proceedings ofthe Third International Conference on Multi-Agent Systems,” pp. 325-332,IEEE Computer Society Press, Los Alamitos. In an auction, the bidderseeks to bid the lowest value, up to a value less than or equal to hisown private value (the actual value which the bidder appraises the goodor service, and above which there is no surplus), that will win theauction. Since competitive bidders can minimize the gains of anotherbidder by exploiting knowledge of the private value attached to the goodor service by the bidder, it is generally a dominant strategy for thebidder to attempt to keep its private value a secret, at least until theauction is concluded, thus yielding strategies that result in thelargest potential gain. On the other hand, in certain situations,release or publication of the private value is a dominant strategy, andcan result in substantial efficiency, that is, honesty in reporting theprivate value results in the maximum likelihood of prospective gain.

SUMMARY AND OBJECTS OF THE INVENTION

The present invention provides a networking system comprising a networkmodel, said model comprising a network parameter estimate; a packetrouter, routing packets in dependence on the model; and an arbitrageagent, to arbitrage a risk that said network parameter estimate isincorrect. The arbitrage agent typically operates with superiorinformation or resources, such that its own estimate of the network at arelevant time is different than that produced by the network model,resulting in an arbitrage opportunity. In this case, arbitrage is notnecessarily meant to indicate a risk-free gain, but rather a reducedrisk potential gain.

The present invention also provides a method for routing acommunication, comprising defining a set of available intermediarynodes, a plurality of members of the set being associated with a riskfactor and an inclusion cost; defining an acceptable communications risktolerance and an acceptable aggregate communications cost; defining aset of network topologies, each network topology employing a subset ofmembers of the set of intermediary nodes, having a communications riskwithin the acceptable communications risk tolerance and a communicationscost within the acceptable aggregate communications cost; and routing acommunication using one of the set of network topologies. In accordingwith this embodiment of the invention, alternate network topologies areavailable through the plurality of nodes, and the selection of a networktopology is based not only on a potential efficiency of a topology, butalso a risk with respect to that topology. Therefore, a less efficienttopology with lower risk may be rationally selected based on a risktolerance. In accordance with this embodiment, the method may furthercomprise the step of arbitraging a risk to increase a cost-benefit.

A further embodiment of the invention provides a method of routing acommunication, comprising: defining a source node, a destination node,and at least two intermediate nodes; estimating a network state of atleast one of the intermediate nodes; arbitraging a risk with respect toan accuracy of the estimate of network state with an arbitrage agent;communicating between said source and said destination; and compensatingsaid at least two intermediate nodes and said agent.

A still further object of the invention is top provide method ofoptimizing relationships between a set of agents with respect to a setof allocable resources, comprising for a plurality of agents,determining at least one of a subjective resource value function, and asubjective risk tolerance value function; providing at least oneresource allocation mechanism, wherein a resource may be allocated onbehalf of an agent in exchange for value; providing at least one risktransference mechanism, wherein a risk may be transferred from one agentto another agent in exchange for value; selecting an optimal allocationof resources and assumption of risk by maximizing, within an errorlimit, an aggregate economic surplus of the putative organization ofagents; accounting for the allocation of resources and allocation ofrisk in accordance with the subjective resource value function and asubjective risk tolerance value function for the selected optimalallocation; and allocating resources and risk in accordance with theselected optimal organization. The resource may comprises, for example,a communication opportunity. The agent may have a subjective resourcevalue for failing to gain an allocation of a resource. Likewise, theagent may have an option or ability to defect from the organization. Theagent may have a multipart resource requirement, wherein an optimalresource allocation requires allocation of a plurality of resourcecomponents. A risk transference agent may be provided to insure a risk.A risk transference agent may be provided to arbitrage a risk. A risktransference agent may be provided which speculatively acquiresresources. The optimal resource allocation may comprise an explicitallocation of a first portion of component resources and an implicitallocation of a second portion of component resources, a risktransference agent undertaking to fulfill the second portion.

In accordance with a still further aspect of the invention, a method ofoptimizing an allocation of resources and deference from contesting theallocation of resources to other agents is provided, comprising:determining a subjective resource value function, and a subjectivedeference value function for an agent with respect to a resourceallocation within a community; selecting an optimal allocation ofresources and deference by maximizing, within an error limit, anaggregate economic surplus of the community; allocating resources inaccordance with the selected optimal organization; and accounting inaccordance with the subjective resource value function, and subjectivedeference value function. This deference value function thus quantifiesin an economic function the deference of one agent to another.

The present invention further provides a method of optimizing anallocation of resources within members of a community, comprising:determining subjective resource value functions for a plurality ofresources for members of the community; selecting an optimal allocationof resources, within an error limit, to maximize an aggregate economicsurplus of the community; charging members of the community inaccordance with the respective subjective resource value functions andmember benefits; allocating at least a portion of the economic surplusresulting from the allocation to members who defer gaining a resourceallocation benefit of the community. The invention further provides amethod of encouraging recruitment of entities into an auction,comprising: defining a set of prevailing parties and a transactionprice; defining an economic surplus from the transaction; anddistributing a portion of the economic surplus to auction participantsnot within the set of prevailing parties, in relation to a magnitude ofan offer. A further aspect of the invention provides a method foroptimizing a market, comprising: recruiting at least four partiescomprising at least one buyer, at least one seller, and at least onedeferring party; matching bidders with offerors to maximize a surplus;and allocating the surplus at least in part to the deferring party, tomotivate deference. In accordance with these embodiments, cooperationwith a resource allocation which might otherwise be rejected, orincentivized the members not to defect from the community. Further, thismechanism incentivizes active participation, which may lead to a moreliquid market and more optimal allocations. The auction may be acombinatorial auction. A plurality of suppliers may transact with aplurality of buyers in a single transaction. In accordance with oneembodiment, only bidders having a significant risk of being within theset of prevailing parties are distributed the portion of the economicsurplus. A portion of the economic surplus may be allocated dependent ona risk of being within the set of prevailing parties. Bidders may berequired to pay a bid fee, for example a non-refundable deposit. Thisbid fee may itself be set, scale with the bid, or set by the bidder,wherein the payback may be a function of the winning bid amount, bidderbid, amount paid, and parameters of other bidders. The economic surplusmay be allocated in such manner to increase the liquidity of a market.

The present invention further provides an ad hoc communication node,comprising: an input for receiving communications and an output forgenerating communications; and a processor, for seeking an optimizationof an ad hoc communication network, said processor determining a networkstate for a portion of the network and estimating a network state for adifferent portion of the network, said processor engaging in atransaction with another node for transferring a risk of an erroneousstate estimation.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings show:

FIG. 1 shows a block diagram of a first embodiment of the presentinvention;

FIG. 2 shows a block diagram of a second embodiment of the presentinvention;

FIG. 3 shows a flowchart of a first method in accordance with thepresent invention;

FIG. 4 shows a flowchart of a second method in accordance with thepresent invention;

FIG. 5 shows a flowchart of a third method in accordance with thepresent invention;

FIG. 6 shows a flowchart of a fourth method in accordance with thepresent invention;

FIG. 7 shows a flowchart of a fifth method in accordance with thepresent invention;

FIG. 8 shows a block diagram of a third embodiment of the presentinvention;

DESCRIPTION OF THE INVENTION

This disclosure incorporates herein by reference the entirety ofWO/2006/029297.

The present invention seeks, among other aspects, to apply aspects ofoptimization theory to the control and arbitration of communities ofresources, that is, elements or agents which operate independently andtherefore cannot be directly controlled without compromise. Rather, thesystem is controlled by providing incentives and disincentives forvarious behaviors and actions seeking to promote an efficient outcomefor the system as a whole, given the external constraints. Each agentthen maximizes its own state based on its own value function, in lightof the incentives and disincentives, resulting in an optimal network.

This optimization employs elements of game theory, and the presentinvention therefore invokes all those elements encompassed within itsscope as applied to the problems and systems presented. The ad hocnetwork of elements typically reside within “communities”, that is, thepresent invention does not particularly seek to apply principles totrivial networks which can be optimized without compromise orarbitration, although its principles may be applicable. The presentinvention therefore applies to the enhancement or optimization ofcommunities. These communities may themselves have various rules,reputation hierarchies, arrangements or cultures, which can be respectedor programmed as a part of the system operation, or merely operate asconstraints on optimization. Thus, in accordance with a game theoreticanalysis, various rules and perceived benefits may be applied toappropriately model the real system, or may be imposed to controlbehavior.

These communities may be formed or employed for various purposes, andtypically interoperate in a “commons” or economy, in which all relevantactions of each member of the community have an effect on others, andthis effect can be quantified or normalized into “economic” terms. Forexample, a typical application of this technology would be to arbitrateaccess to a common or mutually interfering medium, such as acommunications network. By optimizing communications, the greatestaggregate value of communications will generally be achieved, which mayor may not correspond to a greatest aggregate communications bandwidth.For example, both quantity and quality of service may be independent (orsemi-independent) parameters. Thus, the system tends to promote a highquality of service (or at least as high a quality as is required) over abulk volume of service. This, in turn, permits new applications whichdepend on reliable communications.

A general type of economic optimization is a market economy, in whichindependent agents each act to maximize their respective interests. Asubset of a market is an auction, in which a resource is allocated to ahighest valued user or consumer, or a user or consumer acquires aresource at a lowest cost, in a single process. In a market economy,agents may act as peers, and each agent may act as a source of supply orassert a demand. That is, at some market price, a consumer mayrelinquish assets to others and become a supplier. Generally, a consumerhas a higher private value for a resource than a supplier. The suppler,for example, may have a lower cost to obtain the resource, or a lowervalue for consumption of the resource, or both. Peers that both buy andsell a resource may seek to arbitrage, that is, seek to establish acommitted source of supply at a lower cost than a committed purchaser,thus assuring a profit. In order to be effective, arbitrage requiresthat the intermediary have an advantage, or else the ultimate buyerwould transact directly with the ultimate seller. The advantage(s) maybe, for example, information, proprietary elements or methods, location,lower transactional costs, available capital, risk tolerance, storagefacility, or the like.

So long as the advantage does not derive from an economicallyinefficiency monopoly or other structure that artificially and/or“illegally” limits the efficiency of other agents, the arbitrage agentincreases net efficiency of the network. That is, the presence andaction of the arbitrage agent increases the economic surplus of thetransaction and the market in general.

An object of the present invention therefore seeks to overcome theinefficiency of seeking to solve a complex NP-complete optimizationproblem by providing arbitrage opportunities that allow a marketsolution to the optimization which balances optimization cost withintermediary profits. Accordingly, while the net result may deviate froman abstract optimum condition, when one considers the cost of achievingthis abstract optimum, the arbitrage-mediated result is superior interms of market surplus. The availability of arbitrage andintermediaries therefore allows a particular agent to balance its ownoptimization costs against overall gains.

The subject of complexity theory, including combinatorial optimization,and solution of approximation of NP-complete problems, has beenextensively studied. See, e.g., the references set forth in thecombinatorial optimization and auction appendix, which are expresslyincorporated herein by reference.

Assuming a set of rational agents, each agent will seek to locallyoptimize its state to achieve the greatest gains and incur the lowestnet costs. Thus, in a system which seeks to optimize a network of suchagents, by permitting each agent to optimize its local environmentstate, the network may then be approximated by a network of local,environments, each typically comprising a plurality of agents. Thus, inthe same way as the complexity of an NP-complete problem grows inpolynomial space, the simplification of an NP complete problem will alsobe polynomial. While this simplification incurs an inefficiency, eachagent models a proximate region in the space of interest, which tends tobe linear (i.e., superposable) in a preferred embodiment. Agents competewith each other, and therefore the incentive to distort is limited.Likewise, since the preferred simplification of the problem does notimpose a heuristic (i.e., a substitution of a first relatively simpleror more readily analytic algorithm for a second more intractablealgorithm), it does not accordingly distort the incentives from thoseinherent in the basic optimization.

In a simple auction, a role is imposed on an agent, while in a market anagent can choose its own role dynamically, in a series of transactions,dependent on its own value function. In a market, a set of agents havinga resource or requiring a resource seek to interact to reallocate theresource, such that the needs are generally satisfied, up to a “marketclearing price”. That is, individual agents transact to transfer theresource from those with supply to those with demand, at a price betweenthe private values of the two agents, which reallocation occurs untilthe demand ask price is higher than the supply bid price. An auction isdesigned to maximize the economic surplus, which is then typicallyallocated to the more restrictive of the source of supply or consumer orthe sponsor. A market, on the other hand, generally operates to minimizethe gap between bid and ask over an extended period, and thus theeconomic surplus tends to proportionate based on the balance of supplyand demand at the clearing price. The particular reallocation alsodepends on the state of information of each agent, inefficiencies intransfer, and the like.

Where a need or supply is not unitary, one possible means for achievingan optimal solution is a combinatorial auction, in which multiplesuppliers, or multiple consumers, or both, reallocate the resource orportions thereof. Thus, a single need is not met by a single supplier,but rather there are at least three parties to a transaction. The netresult is a competition between parties that raises the potential for aholdout. In fact, one way to circumvent this issue (a “holdout” problem)is to have direct or indirect (bypass) competition for each element. Insuch a circumstance, no agent can effectively demand more than the leastcost alternate(s).

A combinatorial auction (multifactorial optimization, also known as aVickrey Clarke Grove [VCG] Auction) seeks to match, for the entirenetwork of possibilities, the highest valued users with the lowest costsuppliers. This leads, however, to a surplus between the two, which mustbe allocated. In a one-to-many auction, the surplus is generallyallocated to the restricted agent, i.e., the agent(s) with “marketpower”. On the other hand, in an optimal market, the surplus will tendtoward zero. That is, the profit to each party will tend toward acompetitive mean, with higher profits only gained by undertaking higherrisk. In imperfect markets, arbitrage opportunities exist, where profitscan be made by trading the same resource.

In a multihop ad hoc network, a path between source and destinationconsists of a number of legs, with alternate paths available forselection of an optimum. If each node has its own distinct destination,there will likely be competing demands for each intermediatecommunication node.

One way to promote revealing a private value is if the end result of theprocess does not penalize those with aberrantly high or low values. Oneknown method is to compute the result of the process as if the bidder oraskor was not involved, leading to a so-called second price. Thus, thehighest bidder wins, at a price established by a second highest bid. Alowest askor wins, at a price established by the second lowest askor. Ina market, the highest bidder and lowest askor win, with a second pricedependant on a more restrictive of supply and demand. In a combinatorialauction, this may be extended to price each component as if the highestbidder was uninvolved. In one embodiment of the invention, the secondprice applies to both buyer and seller, respectively, with the economicsurplus allocated to other purposes. Thus, in this case, neither partygains the particular benefit of an imbalance of supply and demand. Infact, this perturbs the traditional market somewhat, in that animbalance in supply and demand does not particularly recruit new marketentrants in the same manner as an allocation of the surplus.

Arbitrage

The present invention seeks to model, within a microeconomy, theelements of the real economy which tend to improve efficiency toward“perfection”, that is, a perfect universal balance of supply and demand,for which no strategy (other than bidding a true private value) willproduce a superior result. It is known that combinatorial auctionspermit arbitrage opportunities. See, Andrew Gilpin and Tuomas Sandholm.2004. Arbitrage in Combinatorial Exchanges. AAMAS-04 6th Workshop onAgent Mediated Electronic Commerce (AMEC-VI), New York, N.Y., 2004,expressly incorporated herein by reference.

These efficiency producing elements, paradoxically, are the parasiticelements which thrive off of predictable inefficiencies. That is, bypromoting competition among the parasitic elements, an efficient balanceof optimization of the direct market and optimization of the derivativemarkets will produce an overall superior result to an optimization ofthe direct market alone.

While the use of derivative markets in real economies is well known, theimplementation of these as aspects of microeconomies and isolatedmarkets is not well known, and a part of an embodiment of the presentinvention. For example, in a corporate bankruptcy auction, there areoften resellers present who seek to purchase assets cheaply at a“wholesale” price, and to redistribute them on a less urgent basis or insmaller quantities, or at a different location, or otherwisetransformed, and to sell them at a higher “retail” price. The auctionprice is determined by the number and constitution of bidders, as wellas the possibility of proxy or absentee bidding. In fact, we presumethat the auctioneers themselves are efficient, and that significantlyhigher bid prices are not available in a modified process withoutincurring substantial investment, risk, or delay. Indeed, these premisesin a narrow sense might be false, i.e., a rational auctioneer mightindeed make greater investment, undertake higher risk, or incur greaterdelay. However, this possible inefficiency merely shifts the allocationof the surplus, and to the extent there is a substantial gain to bemade, encourages arbitrage, which in turn encourages competition atsubsequent auctions, leading to at least a partial remediation of theallocation “error” in the long term, over a series of auctions.

Therefore, the market system, with derivative and arbitragepossibilities, and deviations from optimal performance is at leastpartially self-correcting over the long term.

Likewise, because the system has mechanisms for reducing the effects ofimperfections in the presumptions and/or the conformance of a realsystem to the stated mechanisms and applicable rules, particular aspectsof the system which impose administrative or overhead burdens may becircumvented by imposing less restrictive criteria and allowing a “selfcorrecting” mechanism to remediate. Thus, for example, if atheoretically ideal mechanism imposes a 15% burden due to overhead, thusachieving an 85% overall efficiency (100−15=85), while a simplifyingpresumption achieves a result which imposes a 20% efficiency impairmentbut only a 2% overhead factor (100−20−2=78), and an arbitrage mechanismis available to correct the simplified model to gain 12% efficiency withanother 2% overhead (78+12−2), the net result is 88% efficiency, abovethat of the theoretically ideal mechanism.

An arbitrage mechanism seeks to identify inefficiency based on superiorinformation or mechanism, and a pricing or value disparity, and conducta countertrade seeking to exploit the disparity while undertakingrelatively low risk, to increase overall market efficiency. (That is, toultimately reallocate resources from a lower valued holder to a highervalued holder).

An ad hoc network generally presents a case where individual nodes haveimperfect information, and efforts to gain better information invariablylead to increased overhead. Therefore, by intentionally truncating theinformation gathering and discovery aspect of the ad hoc network, aresidual arbitrage opportunity will remain, but the inherentinefficiency of the arbitrage may be less than the correspondingoverhead involved in providing more perfect information to theindividual nodes (i.e., overall arbitrage cost is less than efficiencygain).

As such, a design feature of an embodiment of the invention is toprovide or even encourage arbitrage mechanisms and arbitrageopportunities, in an effort to improve overall system efficiency. Infact, an embodiment of the system is preferably constructed to regularlyprovide arbitrage opportunities which can be conducted with low risk andwith substantial market efficiency gains, and these arbitrageopportunities may be an important part of the operation of theembodiment.

A second opportunity provides risk transference, such as debttransactions, insurance, and market-making, and/or the like. In suchtransactions, a market risk is apparent. Each node, on the other and,has its own subjective risk tolerance. Likewise, the market riskprovides an opportunity for nodes having a high risk tolerance toimprove yield, by trading risk for return. Those nodes which havegenerally greater liquid resources, which inherently have no returnwhile uninvested, and may permit other nodes having lesser resources toborrow, at interest. Because there is a risk of non-payment, nodes mayhave different credit ratings, and this creates an opportunity forcredit rating “agencies” and/or guarantors. In an ad hoc network, thereis also a possibility for delivery failure, which, in turn, provides anopportunity for insurance.

MANET System

Multihop Ad Hoc Networks require cooperation of nodes which arerelatively disinterested in the content being conveyed. Typically, suchdisinterested intermediaries incur a cost for participation, forexample, power consumption or opportunity cost. Economic incentives maybe used to promote cooperation of disinterested intermediaries, alsoknown as recruitment. An economic optimization may be achieved using amarket-finding process, such as an auction. In many scenarios, thedesire for the fairness of an auction is tempered by other concerns,i.e., there are constraints on the optimization which influence priceand parties of a transaction. For example, in military communicationsystems, rank may be deemed an important factor in access to, andcontrol over, the communications medium. A simple process of rank-basedpreemption, without regard for subjective or objective importance, willresult in an inefficient economic distortion. In order to normalize theapplication of rank, one is presented with two options: imposing anormalization scheme with respect to rank to create a unified economy,or providing considering rank using a set of rules outside of theeconomy. One way to normalize rank, and the implicit hierarchyunderlying the rank, is by treating the economy as an object-orientedhierarchy, in which each individual inherits or is allocated a subset ofthe rights of a parent, with peers within the hierarchy operating in apurely economic manner. The extrinsic consideration of rank, outside ofan economy, can be denominated “respect”, which corresponds to thesocietal treatment of the issue, rather than normalizing this factorwithin the economy, in order to avoid unintended secondary economicdistortion. Each system has its merits and limitations.

An economic optimization is one involving a transaction in which allbenefits and detriments can be expressed in normalized terms, andtherefore by balancing all factors, including supply and demand, at aprice, an optimum is achieved. Auctions are well known means to achievean economic optimization between distinct interests, to transfer a goodor right in exchange for a market price. While there are different typesof auctions, each having their limitations and attributes, as a classthese are well accepted as a means for transfer of goods or rights at anoptimum price. Where multiple goods or rights are required in asufficient combination to achieve a requirement, a so-calledVickrey-Clarke-Groves (VCG) auction may be employed. In such an auction,each supplier asserts a desired price for his component. The variouscombinations which meet the requirement are then compared, and thelowest selected. In a combinatorial supply auction, a plurality ofbuyers each seeks a divisible commodity, and each bids its best price.The bidders with the combination of prices which are maximum areselected. In a commodity market, there are a plurality of buyers andsellers, so the auction is more complex. In a market economy, theredistribution of goods or services are typically transferred betweenthose whose value them least to those who value them most. Thetransaction price depends on the balance between supply and demand; withthe surplus being allocated to the limiting factor.

Derivatives, Hedges, Futures and Insurance

In a market economy, the liquidity of the commodity is typically suchthat the gap between bid and ask is small enough that the gap betweenthem is small enough that it is insignificant in terms of preventing atransaction. In a traditional market, the allocation of the surplusoscillates in dependence on whether it is a buyer's or seller's market.Of course, the quantum of liquidity necessary to assure an acceptablylow gap is subjective, but typically, if the size of the market issufficient, there will be low opportunity for arbitrage, or at least acompetitive market for arbitrage. The arbitrage may be either in thecommodity, or options, derivatives, futures, or the like.

In a market for communications resources, derivatives may providesignificant advantages over a simple unitary market for directtransactions. For example, a node may wish to procure a reliablecommunications pathway (high quality of service or QoS) for an extendedperiod. Thus, it may seek to commit resources into the future, and notbe subject to future competition for or price fluctuation of thoseresources, especially being subject to a prior broadcast of its ownprivate valuation and a potential understanding by competitors of thepresumed need for continued allocation of the resources. Thus, forsimilar reasons for the existence of derivative, options, futures, etc.markets in the real economy, their analogy may be provided within acommunications resource market.

In a futures market analogy, an agent seeks to procure its long-term orbulk requirements, or seeks to dispose of its assets in advance of theiravailability. In this way, there is increased predictability, and lesspossibility of self-competition. It also allows transfer of assets inbulk to meet an entire requirement or production lot capability, thusincreasing efficiency and avoiding partial availability or disposal.

One issue in mobile ad hoc networks is accounting for mobility of nodesand unreliability of communications. In commodities markets, one optionis insurance of the underlying commodity and its production. The analogyin communications resource markets focuses on communicationsreliability, since one aspect of reliability, nodal mobility is“voluntary” and not typically associated with an insurable risk. On theother hand, the mobility risk may be mitigated by an indemnification. Incombination, these, and other risk transfer techniques, may providemeans for a party engaged in a communications market transaction tomonetarily compensate for risk tolerance factors. An agent in the markethaving a low risk tolerance can undertake risk transference, at someadditional but predetermined transaction costs, while one with a highrisk tolerance can “go bare” and obtain a lower transaction cost, orundertake third party risk for profit.

Insurance may be provided in various manners. For example, somepotential market participants may reserve wealth, capacity or demand fora fee, subject to claim in the event of a risk event. In other cases, aseparate system may be employed, such as a cellular carrier, to step in,in the event that a lower cost resource is unavailable (typically forbandwidth supply only). A service provider may provide risk-relatedallocations to network members in an effort to increase perceivednetwork stability; likewise, if the network is externally controlled,each node can be subject to a reserve requirements which is centrally(or hierarchally) allocated.

If an agent promises to deliver a resource, and ultimately fails todeliver, it may undertake an indemnification, paying the buyer an amountrepresenting “damages” or “liquidated damages”, the transaction cost ofbuyer, e.g., the cost or estimated cost of reprocurement plus lostproductivity and/or gains. Likewise, if an agent fails to consumeresources committed to it, it owes the promised payment, less the resalevalue of the remaining resources, if any. An indemnificationinsurer/guarantor can undertake to pay the gap on behalf of thedefaulting party. Typically, the insurer may, but need not be, a normalagent peer.

Hedge strategies may also be employed in known manner.

In order for markets to be efficient, there must be a possibility ofbeneficial use or resale of future assets. This imposes some complexity,since the assets are neither physical nor possessed by the intermediary.However, cryptographic authentication of transactions may provide someremedy. On the other hand, by increasing liquidity and providingmarket-makers, the transaction surplus may be minimized, and thus thereallocation of the surplus as discussed above minimized. Likewise, in amarket generally composed of agents within close proximity, theinterposition of intermediaries may result in inefficiencies rather thanefficiencies, and the utility of such complexity may better come fromthe facilitation of distant transactions. Thus, if one presumes slow,random nodal mobility, little advantage is seen from liquid resource anddemand reallocation. On the other hand, if an agent has a predefineditinerary for rapidly relocating, it can efficiently conducttransactions over its path, prearranging communication paths, and thusproviding trunk services. Thus, over a short term, direct multihopcommunications provide long-distance communications of bothadministrative and content data. On the other hand, over a longer term,relocation of agents may provide greater efficiency for transport ofadministrative information, increasing the efficiency of content datacommunications over the limited communications resources, especially ifa store-and-forward paradigm is acceptable.

It is noted that in an economy having persistent and deep use offinancial derivatives, a stable currency is preferred, and the decliningvalue credit discussed above would provide a disincentive to agents whomight otherwise take risks over a long time-frame. It is possible,however, to distinguish between credits held by “consumers” and thoseheld by “arbitrageurs” or institutions, with the former having adeclining value but can be spent, and those which have a stable valuebut must be first converted (at some possible administrative cost) forconsumer use.

Bandwidth Auction

A previous scheme proposes the application of game theory in the controlof multihop mobile ad hoc networks according to “fair” principles. Inthis prior scheme, nodes seeking to control the network (i.e., are“buyers” of bandwidth), conduct an auction for the resources desired.Likewise, potential intermediate nodes conduct an auction to supply theresources. The set of winning bidders and winning sellers is optimizedto achieve the maximum economic surplus. Winning bidders pay the maximumbid price or second price, while winning sellers receive their winningask or second price. The remaining surplus is redistributed among thewinners and losing bidders, whose cooperation and non-interference withthe winning bidders is required for network operation. The allocation ofthe portion to losing bidders is, for example, in accordance with theirproportionate bid for contested resources, and for example, limited tothe few (e.g., 3) highest bidders or lowest offerors. The winning bidsare determined by a VCG combinatorial process. The result is an optimumnetwork topology with a reasonable, but by no means the only, fairnesscriterion, while promoting network stability and utility.

The purpose of rewarding losers is to encourage recruitment, andtherefore market liquidity. In order to discourage strategic losingbids, one possible option is to impose a statistical noise on theprocess to increase the risk that a strategically losing bid will be awinning bid. Another way is to allocate the available surpluscorresponding to the closeness of the losing bid to the winning bid, notmerely on its magnitude. Alternately, a “historical” value for theresource may be established, and an allocation made only if the bid isat or above the trailing mean value. Further, the loser's allocation maybe dependent on a future bid with respect to a corresponding resource ator above the prior value. In similar manner, various algorithms forsurplus allocation may be designed to encourage recruitment of agentsseeking to win, while possibly discouraging bidders who have littlerealistic possibility of winning. Bidders who do not seek to win imposean inefficiency on the network, for example requiring other agents tocommunicate, evaluate, acknowledge, and defer to these bids. Therefore,a relatively small bidding fee may be imposed in order to assert a bid,which may be used to increase the available surplus to be allocatedbetween the winning and top losing bidders.

As discussed above, risk may be a factor in valuing a resource. Theauction optimization may therefore be normalized or perturbed independence on an economic assessment of a risk tolerance, either basedon a personal valuation, or based on a third party valuation(insurance/indemnification). Likewise, the optimization may also bemodified to account for other factors.

Thus, one issue with such a traditional scheme for fair allocation ofresources is that it does not readily permit intentional distortions,that is, the system is “fair”. However, in some instances, a relativelyextrinsic consideration to supply and subjective demand may be a corerequirement of a system. For example, in military systems, it istraditional and expected that higher military rank will provide accessto and control over resources on a favored basis. (Note that, incontrast to an example discussed elsewhere herein, this favoritism isnot enforced by a hierarchal wealth generation distribution). Incivilian systems, emergency and police use may also be consideredprivileged. However, by seeking to apply economic rules to this access,a number of issues arise. Most significantly, as a privileged userdisburses currency, this is distributed to unprivileged users, leadingto an inflationary effect and comparative dilution of the intendedprivilege. If the economy is real, that is the currency is linked to areal economy, this grant of privilege will incur real costs, which isalso not always an intended effect. If the economy is synthetic, thatis, it is unlinked to external economies, then the redistribution ofwealth within the system can grant dramatic and potentially undesiredcontrol to a few nodes, potentially conveying the privilege to thoseundeserving, except perhaps due to fortuitous circumstances such asbeing in a critical location or being capable of interfering with acrucial communication.

Two different schemes may be used to address this desire for botheconomic optimality and hierarchal considerations. One scheme maintainsoptimality and fairness within the economic structure, but applies agenerally orthogonal consideration of “respect” as a separate factorwithin the operation of the protocol. Respect is a subjective factor,and thus permits each bidder to weight its own considerations. It isfurther noted that Buttyan et al. have discussed this factor as a partof an automated means for ensuring compliance with network rules, in theabsence of a hierarchy. Levente Buttyan and Jean-Pierre Hubaux, Nuglets:a Virtual Currency to Stimulate Cooperation in Self-Organized Mobile AdHoc Networks, Technical Report DSC/2001/004, EPFL-DI-ICA, January 2001,incorporated herein by reference. See, P. Michiardi and R. Molva, CORE:A collaborative reputation mechanism to enforce node cooperation inmobile ad hoc networks, In B. Jerman-Blazic and T. Klobucar, editors,Communications and Multimedia Security, IFIP TC6/TC11 Sixth JointWorking Conference on Communications and Multimedia Security, Sep.26-27, 2002, Portoroz, Slovenia, volume 228 of IFIP ConferenceProceedings, pages 107-121. Kluwer Academic, 2002; Sonja Buchegger andJean-Yves Le Boudec, A Robust Reputation System for P2P and MobileAd-hoc Networks, Second Workshop on the Economics of Peer-to-PeerSystems, June 2004; Po-Wah Yau and Chris J. Mitchell, Reputation Methodsfor Routing Security for Mobile Ad Hoc Networks; Frank Kargl, AndreasKlenk, Stefan Schlott, and Micheal Weber. Advanced Detection of Selfishor Malicious Nodes in Ad Hoc Network. The 1st European Workshop onSecurity in Ad-Hoc and Sensor Networks (ESAS 2004); He, Qi, et al.,SORI: A Secure and Objective Reputation-based Incentive Scheme forAd-Hoc Networks, IEEE Wireless Communications and Networking Conference2004, each of which is expressly incorporated herein by reference.

The bias introduced in the system operation is created by an assertionby one claiming privilege, and deference by one respecting privilege.One way to avoid substantial economic distortions is to require that thepayment made be based on a purely economic optimization, while selectingthe winner based on other factors. In this way, the perturbations of theauction process itself is subtle, that is, since bidders realize thatthe winning bid may not result in the corresponding benefit, but incursthe publication of private values and potential bidding costs, there maybe perturbation of the bidding strategy from optimal. Likewise, sincethe privilege is itself unfair and predictable, those with lowerprivilege ratings will have greater incentive to defect from, or actagainst, the network. Therefore, it is important that either theassertion of privilege be subjectively reasonable to those who mustdefer to it, or the incidence or impact of the assertions be uncommon orhave low anticipated impact on the whole. On the other hand, theperturbation is only one-sided, since the payment is defined by thenetwork absent the assertion of privilege.

In the extreme case, the assertion of privilege will completelyundermine the auction optimization, and the system will be prioritizedon purely hierarchal grounds, and the pricing non-optimal orunpredictable. This condition may be acceptable or even efficient inmilitary systems, but may be unacceptable where the deference isvoluntary and choice of network protocol is available, i.e., defectionfrom the network policies is an available choice.

It is noted that those seeking access based on respect, must still makean economic bid. This bid, for example, should be sufficient in the casethat respect is not afforded, for example, from those of equal rank orabove, or those who for various reasons have other factors that overridethe assertion of respect. Therefore, one way to determine the amount ofrespect to be afforded is the self-worth advertised for the resourcesrequested. This process therefore may minimize the deviation fromoptimal and therefore promotes stability of the network. It is furthernoted that those who assert respect based on hierarchy typically haveavailable substantial economic resources, and therefore it is largely adesire to avoid economic redistribution rather than an inability toeffect such a redistribution, that compels a consideration of respect.

In a combinatorial auction, each leg of a multihop link is separatelyacquired and accounted. Therefore, administration of the process isquite involved. That is, each bidder broadcasts a set of bids for theresources required, and an optimal network with maximum surplus isdefined. Each leg of each path is therefore allocated a value. In thiscase, it is the winning bidder who defers based on respect, since theother resources are compensated equally and therefore agnostic.

Thus, if pricing is defined by the economic optimization, then therespect consideration requires that a subsidy be applied, either as anexcess payment up to the amount of the winning bid, or as a discountprovided by the sellers, down to the actually bid value.

Since the pricing is dependent on the network absent the respectconsideration, there is an economic deficit or required subsidy. In somecases, the respected bidder simply pays the amount required, in excessof its actual bid. If we presume that the respected bidder could have orwould have outbid the winning bidder, it then pays the third price,rather than the second price. If the respected bidder does not have, orwill not allocate the resources, then the subsidy must come from theothers involved. On one hand, since the respect in this case may bedefined by the otherwise winning bidder, this bidder, as an element ofits respect, may pay the difference. However, this cost (both the losteconomic gains of the transaction and the subsidy) will quicklydisincentivize any sort of grant of respect. The recipients could alsoprovide a discount; however this would require consent of both thewinning bidder and the recipients, making concluding the transactionmore difficult. One other possibility is to request “donations” fromnearby nodes to meet the subsidy, a failure of which undermines theassertion of respect.

Another alternate is to assume that there is a surplus between theaggregate winning bid and the aggregate cost, and so long as the bidderclaiming respect pays the minimum cost, then the system remainsoperable, although the benefits of surplus allocation are lost, and allaffected nodes must defer to this respect mechanism. In this case, it ismore difficult to arbitrate between competing demands for respect,unless a common value function is available, which in this case wepresume is not available.

The node demanding respect may have an impact on path segments outsideits required route and the otherwise optimal interfering routes; andthus the required payment to meet the differential between the optimumnetwork and the resulting network may thus be significant.

It is noted that, in the real economy, where the U.S. Governmentallocates private resources, it is required to pay their full value.This model appears rational, and therefore a preferred system requires anode claiming privilege and gaining a resulting benefit to pay thewinning bid value (as an expression of market value), and perhaps inaddition pay the winning bidder who is usurped its anticipated benefit,that is, the difference in value between the second price and itspublished private valuation, this having an economically neutral affect,but also requiring a respected node to potentially possess substantialwealth.

A further possible resolution of this issue provides for an assessmentof an assertion of respect by each involved node. Since the allocationof respect is subjective, each bidder supplies a bid, as well as anassertion of respect. Each other node receives the bids and assertions,and applies a weighting or discount based on its subjective analysis ofthe respect assertion. In this case, the same bid is interpreteddifferently by each supplier, and the subjective analysis must beperformed by or for each supplier. By converting the respect assertioninto a subjective weighting or discount, a pure economic optimizationmay then be performed, with the subjectively perturbed result by eachnode reported and used to compute the global optimization.

An alternate scheme for hierarchal deference is to organize the economyitself into a hierarchy, as discussed in the first example. In ahierarchy, a node has one parent and possibly multiple children. At eachlevel, a node receives an allocation of wealth from its parent, anddistributes all or a portion of its wealth to children. A parent ispresumed to control its children, and therefore can allocate theirwealth or subjective valuations to its own ends. When nodes representingdifferent lineages must be reconciled, one may refer to the commonancestor for arbitration, or a set of inherited rules to define thehierarchal relationships.

In this system, the resources available for reallocation betweenbranches of the hierarchy depend on the allocation by the commongrandparent, as well as competing allocations within the branch. Thissystem presumes that children communicate with their parents and areobedient. In fact, if the communication presumption is violated, onemust then rely on a priori instructions, which may not be sufficientlyadaptive to achieve an optimal result. If the obedience presumption isviolated, then the hierarchal deference requires an enforcementmechanism within the hierarchy. If both presumptions are simultaneouslyviolated, then the system will likely fail, except on a voluntary basis,with results similar to the “reputation” scheme described herein.

Thus, it is possible to include hierarchal deference as a factor inoptimization of a multihop mobile ad hoc network, leading tocompatibility with tiered organizations, as well as with sharedresources.

Application of Game Theory to Ad Hoc Networks

There are a number of aspects of ad hoc network control which may beadjusted in accordance with game theoretic approaches. An example of theapplication of game theory to influence system architecture arises whencommunications latency is an issue. A significant factor in latency isthe node hop count. Therefore, a system may seek to reduce node hopcount by using an algorithm other than a nearest neighbor algorithm,bypassing some nodes with longer distance communications. In analyzingthis possibility, one must not only look at the cost to the nodesinvolved in the communication, but also the cost to nodes which areprevented from simultaneously accessing the network due to interferinguses of network resources. As a general proposition, the analysis of thenetwork must include the impact of each action, or network state, onevery node in the system, although simplifying presumptions may beappropriate where information is unavailable, or the anticipated impactis trivial.

Game theory is readily applied in the optimization of communicationsroutes through a defined network, to achieve the best economic surplusallocation. In addition, the problem of determining the networktopology, and the communications themselves, are ancillary, though real,applications of game theory. Since the communications incidental to thearbitration require consideration of some of the same issues as theunderlying communications, corresponding elements of game theory mayapply at both levels of analysis. Due to various uncertainties, theoperation of the system is stochastic. This presumption, in turn, allowsestimation of optimality within a margin of error, simplifyingimplementation as compared to a rigorous analysis without regard tostatistical significance.

There are a number of known and proven routing models proposed forforwarding of packets in ad hoc networks. These include Ad Hoc On-DemandDistance Vector (AODV) Routing, Optimized Link State Routing Protocol(OLSR), Dynamic Source Routing Protocol (DSR), and TopologyDissemination Based on Reverse-Path Forwarding (TBRPF).

There can be significant differences in optimum routing depending onwhether a node can modulate its transmit power, which in turn controlsrange, and provides a further control over network topology. Likewise,steerable antennas, antenna arrays, and other forms of multiplexingprovide further degrees of control over network topology. Note that theprotocol-level communications are preferably broadcasts, whileinformation conveyance communications are typically point-to-point.Prior studies typically presume a single transceiver, with a singleomnidirectional antenna, operating according to in-band protocol data,for all communications. The tradeoff made in limiting system designsaccording to these presumptions should be clear.

It is the general self-interest of a node to conserve its own resources,maintain an opportunity to access network resources, while consumingwhatever resource of other nodes as it desires. Clearly, this presents asignificant risk of the “tragedy of the commons”, in which selfishindividuals fail to respect the very basis for the community they enjoy,and a network of rational nodes operating without significant incentivesto cooperate would likely fail. On the other hand, if donating a node'sresources generated a sufficient associated benefit to that node, whileconsuming network resources imposed a sufficient cost, stability andreliability can be achieved. So long as the functionality is sufficientto meet the need, and the economic surplus is “fairly” allocated, thatis, the cost incurred is less than the private value of the benefit, andthat cost is transferred as compensation to those burdened in an amountin excess of their incremental cost, adoption of the system shouldincrease stability. In fact, even outside of these bounds, the systemmay be more stable than one which neither taxes system use nor rewardsaltruistic behavior. While the basic system may be a zero sum system,and over time, the economic effects will likely average out (assumingsymmetric nodes), in any particular instance, the incentive for selfishbehavior by a node will be diminished.

One way to remedy selfish behavior is to increase the cost of actingthis way, that is, to impose a cost or tax for access to the network. Ina practical implementation, however, this is problematic, since underlightly loaded conditions, the “value” of the communications may notjustify a fixed cost which might be reasonable under other conditions,and likewise, under heavier loads, critical communications may still bedelayed or impeded. A variable cost, dependent on relative “importance”,may be imposed, and indeed, as alluded to above, this cost may be marketbased, in the manner of an auction. In a multihop network, such anauction is complicated by the requirement for a distribution of paymentswithin the chain of nodes, with each node having potential alternatedemands for its cooperation. The market-based price-finding mechanismexcludes nodes which ask a price not supported by its market position,and the auction itself may comprise a value function encompassingreliability, latency, quality of service, or other non-economicparameters, expressed in economic terms. The network may further requirecompensation to nodes which must defer communications because ofinconsistent states, such as in order to avoid interference orduplicative use of an intermediary node, and which take no direct partin the communication. It is noted that the concept of the winner of anauction paying the losers is not generally known, and indeed somewhatcounterintuitive. Indeed, the effect of this rule perturbs thetraditional analysis framework, since the possibility of a payment fromthe winner to the loser alters the allocation of economic surplusbetween the bidder, seller, and others. Likewise, while the cost to theinvolved nodes may be real, the cost to the uninvolved nodes may besubjective. While it would appear that involved nodes would generally bebetter compensated than uninvolved nodes, the actual allocation orreallocation of wealth according to the optimization may result in adifferent outcome.

The network provides competitive access to the physical transportmedium, and cooperation with the protocol provides significantadvantages over competition with it. Under normal circumstances, a welldeveloped ad hoc network system can present as a formidable coordinatedcompetitor for access to contested bandwidth by other systems, whilewithin the network, economic surplus is optimized. Thus, a nodepresented with a communications requirement is presented not with thesimple choice to participate or abstain, but rather whether toparticipate in an ad hoc network with predicted stability and mutualbenefit, or one with the possibility of failure due to selfish behavior,and non-cooperation. Even in the absence of a present communicationrequirement, a network which rewards cooperative behavior may bepreferable to one which simply expects altruism without rewarding it.

The protocol may also encompass the concept of node reputation, that is,a positive or negative statement by others regarding the node inquestion. P. Michiardi and R. Molva. Core: A collaborative reputationmechanism to enforce node cooperation in mobile ad hoc networks. InCommunication and Multimedia Security 2002 Conference, 2002. Thisreputation may be evaluated as a parameter in an economic analysis, orapplied separately, and may be anecdotal or statistical. In any case, ifaccess to resources and payments are made dependent on reputation, nodeswill be incentivized to maintain a good reputation, and avoid generatinga bad reputation. Therefore, by maintaining and applying the reputationin a manner consistent with the community goals, the nodes are compelledto advance those goals in order to benefit from the community. Gametheory distinguishes between good reputation and bad reputation. Nodesmay have a selfish motivation to assert that another node has a badreputation, while it would have little selfish motivation, absentcollusion, for undeservedly asserting a good reputation. On the otherhand, a node may have a selfish motivation in failing to reward behaviorwith a good reputation.

Economics and reputation may be maintained as orthogonal considerations,since the status of a node's currency account provides no informationabout the status of its reputation.

This reputation parameter may be extended to encompass respect, that is,a subjective deference to another based on an asserted or imputedentitlement. While the prior system uses reputation as a factor toensure compliance with system rules, this can be extended to provideddeferential preferences either within or extrinsic to an economy. Thus,in a military hierarchy, a relatively higher ranking official can assertrank, and if accepted, override a relatively lower ranking bidder at thesame economic bid. For each node, an algorithm is provided to translatea particular assertion of respect (i.e., rank and chain of command) intoan economic perturbation. For example, in the same chain of command,each difference in rank might be associated with a 25% compoundeddiscount, when compared with other bids, i.e.

B ₁ =B ₀×10(1+0.25×ΔR),

Wherein B₁ is the attributed bid, B₀ is the actual bid, and ΔR is thedifference in rank, positive or negative.

Outside the chain of command, a different, generally lower, discount(dNCOC) may be applied, possibly with a base discount as compared to allbids within the chain of command (dCOC), i.e.,

B ₁ =B ₀×10(1+dCOC+dNCOC×ΔR).

The discount is applied so that higher ranking officers pay less, whilelower ranking officers pay more. Clearly, there is a high incentive foreach bid to originate from the highest available commander within thechain of command, and given the effect of the perturbation, for rankingofficers to “pull rank” judiciously.

The Modified VCG Auction

A so-called Vickrey-Clarke-Groves, or VCG, auction is a type of auctionsuitable for bidding, in a single auction, for the goods or services ofa plurality of offerors, as a unit. Vickrey, W. (1961).Counterspeculation, auctions, and competitive sealed tenders, Journal ofFinance 16, 8-37; Clarke, E. H. (1971). Multipart pricing of publicgoods, Public Choice 11, 17-33.

In the classic case, each bidder bids a value vector for each availablecombination of goods or services. The various components and associatedask price are evaluated combinatorially to achieve the minimum sum tomeet the requirement. The winning bid set is that which produces themaximum value of the accepted bids, although the second (Vickrey) priceis paid. In theory, the Vickrey price represents the maximum state ofthe network absent the highest bidder, so that each bidder isincentivized to bit its private value, knowing that its pricing will bedependent not on its own value, but the subjective value applied byothers. In the present context, each offeror submits an ask price(reserve) or evaluatable value function for a component of thecombination. If the minimum aggregate to meet the bid requirement is notmet, the auction fails. If the auction is successful, then the set ofofferors selected is that with the lowest aggregate bid, and they arecompensated that amount.

The VCG auction is postulated as being optimal for allocation ofmultiple resources between agents. It is “strategyproof” and efficient,meaning that it is a dominant strategy for agents to report their truevaluation for a resource, and the result of the optimization is anetwork which maximizes the value of the system to the agents. Gametheory also allows an allocation of cost between various recipients of abroadcast or multicast. That is, the communication is of value to aplurality of nodes, and a large set of recipient nodes may efficientlyreceive the same information. This allocation from multiple bidders tomultiple sellers is a direct extension of VCG theory, and a similaralgorithm may be used to optimize allocation of costs and benefit.

The principal issue involved in VCG auctions is that the computationalcomplexity of the optimization grows with the number of buyers and theirdifferent value functions and allocations. While various simplifyingpresumptions may be applied, studies reveal that these simplificationsmay undermine the VCG premise, and therefore do not promote honesty inreporting the buyer's valuation, and thus are not “strategyproof”, whichis a principal advantage of the VCG process.

The surplus, i.e., gap between bid and ask, is then available tocompensate the deferred bidders. This surplus may be, for example,distributed proportionately to the original bid value of the bidder,thus further encouraging an honest valuation of control over theresource. Thus, if we presume that a bidder may have an incentive toadopt a strategy in which it shaves its bid to lower values, anadditional payoff dependent on a higher value bid will promote higherbides and disincentivize shaving. On the other hand, it would beinefficient to promote bidding above a bidder's private value, andtherefore care must be exercised to generally avoid this circumstance.In similar manner, potential offerors may be compensated for low bids,to promote availability of supply. It is noted that, by broadcastingsupply and demand, fault tolerance of the network is improved, since inthe event that an involved node becomes unavailable, a competing node orset of nodes for that role may be quickly enlisted.

The optimization is such that, if any offeror asks an amount that is toohigh, it will be bypassed in favor of more “reasonable” offerors. Sincethe bidder pays the second highest price, honesty in bidding the fullprivate value is encouraged. The distribution of the surplus to losingbidders, which exercise deference to the winner, is proportional to theamount bid, that is, the reported value.

In a scenario involving a request for information meeting specifiedcriteria, the auction is complicated by the fact that the informationresource content is unknown to the recipient, and therefore the bid isblind, that is, the value of the information to the recipient isindeterminate. However, game theory supports the communication of avalue function or utility function, which can then be evaluated at eachnode possessing information to be communicated, to normalize its valueto the requestor. Fortunately, it is a dominant strategy in a VCGauction to communicate a truthful value, and therefore broadcasting theprivate value function, to be evaluated by a recipient, is notuntenable. In a mere request for information conveyance, such as theintermediate transport nodes in a multihop network, or in a cellularnetwork infrastructure extension model, the bid may be a true (resolved)value, since the information content is not the subject of the bidding;rather it is the value of the communications per se, and the biddingnode can reasonably value its bid.

Game theory also allows an allocation of cost between various recipientsof a broadcast or multicast. That is, in many instances, informationwhich is of value to a plurality of nodes, and a large set of recipientnodes may efficiently receive the same information. This allocation is adirect extension of VCG theory.

Operation of Protocol

The preferred method for acquiring an estimate of the state of thenetwork is through use of a proactive routing protocol. Thus, in orderto determine the network architecture state, each node must broadcastits existence, and, for example, a payload of information including itsidentity, location, itinerary (navigation vector) and “information valuefunction”. Typically, the system operates in a continuous set of states,so that it is reasonable to commence the process with an estimate of thestate based on prior information. Using an in-band or out-of-bandpropagation mechanism, this information must propagate to a networkedge, which may be physically or artificially defined. If all nodesoperate with a substantially common estimation of network topology, onlydeviations from previously propagated information need be propagated. Onthe other hand, various nodes may have different estimates of thenetwork state, allowing efficiency gains through exploitation ofsuperior knowledge as compared with seeking to convey full network stateinformation to each node.

A CSMA scheme may be used for the protocol-related communicationsbecause it is relatively simple and robust, and well suited for ad hoccommunications in lightly loaded networks. We presume that the networkis willing to tolerate protocol related inefficiency, and therefore thatprotocol communications can occur in a lightly loaded network even ifthe content communications are saturated. An initial node transmitsusing an adaptive power protocol, to achieve an effective transmitrange, for example, of greater than an average internodal distance, butnot encompassing the entire network. This distance therefore promotespropagation to a set of nearby nodes, without unnecessarily interferingwith communications of distant nodes and therefore allowing this task tobe performed in parallel in different regions. Neighboring nodes alsotransmit in succession, providing sequential and complete protocolinformation propagation over a relevance range, for example 3-10 maximumrange hops.

If we presume that there is a spatial limit to relevance, for example, 5miles or 10 hops, then the network state propagation may be so limited.Extending the network to encompass a large number of nodes willnecessarily reduce the tractability of the optimization, and incur anoverhead which may be inefficient. Each node preferably maintains alocal estimate of relevance. This consideration is accommodated, alongwith a desire to prevent exponential growth in protocol-related datatraffic, by receiving an update from all nodes within a node's networkrelevance boundary, and a state variable which represents an estimate ofrelevant status beyond the arbitrarily defined boundary. The propagationof network state may thus conveniently occur over a finite number ofhops, for example 3-10. In a dense population of nodes, such as in acity, even a single maximum range communication may result in a largenumber of encompassed nodes. On the other hand, in a desertedenvironment, there may be few or no communications partners, at anytime.

Under conditions of relatively high nodal densities, the system mayemploy a zone strategy, that is, proximate groups of nodes are istreated as an entity or cluster for purposes of external stateestimation, especially with respect to distant nodes or zones. In fact,a supernode may be nominated within a cluster to control externalcommunications for that cluster. Such a presumption is realistic, sinceat extended distances, geographically proximate nodes may be modeled asbeing similar or inter-related, while at close distances, andparticularly within a zone in which all nodes are in directcommunication, inter-node communications may be subject to mutualinterference, and can occur without substantial external influence.Alternately, it is clear that to limit latencies and communicationrisks, it may be prudent to bypass nearby and neighboring nodes, thustrading latency for power consumption and overall network capacity.Therefore, a hierarchal scheme may be implemented to geographicallyorganize the network at higher analytical levels, and geographic cellsmay cooperate to appear externally as a single coordinated entity.

In order to estimate a network edge condition, a number of presumptionsmust be made. The effect of an inaccurate estimate of the network edgecondition typically leads to inefficiency, while inordinate efforts toaccurately estimate the network edge condition may also lead toinefficiency. Perhaps the best way to achieve compromise is to have aset of adaptive presumptions or rules, with a reasonable starting point.For example, in a multihop network, one might arbitrarily set a networkedge the maximum range of five hops of administrative data using a 95%reliable transmission capability. Beyond this range, a set of stateestimators is provided by each node for its surroundings, which are thencommunicated up to five hops (or the maximum range represented by fivehops). This state estimator is at least one cycle old, and by the timeit is transferred five hops away, it is at least six cycles old.Meanwhile, in a market economy, each node may respond to perceivedopportunities, leading to a potential for oscillations if a time-elementis not also communicated. Thus, it is preferred that the network edgestate estimators represent a time-prediction of network behavior undervarious conditions, rather than a simple scalar value or instantaneousfunction.

For example, each node may estimate a network supply function and anetwork demand function, liquidity estimate and bid-ask gap for itsenvironment, and its own subjective risk tolerance, if separatelyreported; the impact of nodes closer than five hops may then besubtracted from this estimate to compensate for redundant data. Further,if traffic routes are identifiable, which would correspond in a physicalsetting of highways, fixed infrastructure access points, etc., a stateestimator for these may be provided as well. As discussed above, nodesmay bid not only for their own needs or resources, but also to act asmarket-makers or merchants, and may obtain long term commitments(futures and/or options) and employ risk reduction techniques (insuranceand/or indemnification), and thus may provide not only an estimate ofnetwork conditions, but also “guaranty” this state.

A node seeking to communicate within the five hop range needs toconsider the edge state estimate only when calculating its own supplyand demand functions, bearing in mind competitive pressures fromoutside. On the other hand, nodes seeking resources outside the five hoprange must rely on the estimate, because a direct measurement oracquisition of information would require excess administrativecommunications, and incur an inefficient administrative transaction.Thus, a degree of trust and reliance on the estimate may ensue, whereina node at the arbitrary network edge is designated as an agent for theprincipal in procuring or selling the resource beyond its own sphere ofinfluence, based on the provided parameters. The incentive for a node toprovide misinformation is limited, since nodes with too high a reportedestimate value lose gains from competitive sale transactions, and indeedmay be requested to be buyers, and vice versa. While this model maycompel trading by intermediary nodes, if the information communicatedaccurately represents the network state, an economic advantage willaccrue to the intermediary participating, especially in a non-powerconstrained, unlicensed spectrum node configuration.

It should be borne in mind that the intended administration of thecommunications is an automated process, with little human involvement,other than setting goals, risk tolerance, cost constraints, etc. In apurely virtual economy with temporally declining currency value, thedetriment of inaccurate optimizations is limited to reduced nodalefficiency, and with appropriate adaptivity, the system can learn fromits “mistakes”. (A defined decline in currency value tends to define thecost constraints for that node, since wealth cannot be accumulated noroverspent).

A supernode within a zone may be selected for its superior capability,or perhaps a central location. The zone is defined by a communicationrange of the basic data interface for communications, with the controlchannel preferably having a longer range, for example at least doublethe normal data communications range. Communications control channeltransmitters operate on a number of channels, for example at least 7,allowing neighboring zones in a hexagonal tiled array to communicatesimultaneously without interference. In a geographic zone system,alternate zones which would otherwise be interfering may use an adaptivemultiplexing scheme to avoid interference. All nodes may listen on allcontrol channels, permitting rapid analysis and propagation of controlinformation. As discussed elsewhere herein, directional antennas ofvarious types may be employed, although it is preferred that out-of-bandcontrol channels employ omnidirectional antennas, having a generallylonger range (and lower data bandwidth) than the normal datacommunications channels, in order to have a better chance to disseminatethe control information to potentially interfering sources, and to allowcoordination of nodes more globally.

In order to effectively provide decentralized control, either each nodemust have a common set of information to allow execution of an identicalcontrol algorithm, or nodes defer to the control signals of other nodeswithout internal analysis for optimality. A model of semi-decentralizedcontrol is also known, in which dispersed supernodes are nominated asmaster, with other topologically nearby nodes remaining as slave nodes.In the pure peer network, relatively complete information conveyance toeach node is required, imposing a relatively high overhead. In amaster-slave (or supernode) architecture, increased reliance on a singlenode trades-off reliability and robustness (and other advantages of purepeer-to-peer networks) for efficiency. A supernode within a cellularzone may be selected for its superior capability, or perhaps is at acentral location or is immobile.

Once each control node (node or supernode) has an estimate of networktopology, the next step is to optimize network channels. According toVCG theory, each agent has an incentive to broadcast its truthful valueor value function for the scarce resource, which in this case, iscontrol over communications physical layer, and or access toinformation. This communication can be consolidated with the networkdiscovery transmission. Each control node then performs a combinatorialsolution to select the optimum network configuration from thepotentially large number of possibilities, which may include issues oftransmit power, data rate, path, timing, reliability and risk criteria,economic and virtual economic costs, multipath and redundancy, etc., forthe set of simultaneous equations according to VCG theory (or extensionsthereof). This solution should be consistent between all nodes, and theeffects of inconsistent solutions may be resolved by collision sensing,and possibly an error/inconsistency detection and correction algorithmspecifically applied to this type of information. Thus, if each node hasrelatively complete information, or accurate estimates for incompleteinformation, then each node can perform the calculation and derive aclosely corresponding solution, and verify that solutions reported byothers are reasonably consistent to allow or promote reliance thereon.

As part of the network mapping, communications impairment andinterference sources are also mapped. GPS assistance may be particularlyuseful in this aspect. Where network limitations are caused byinterfering communications, the issue is a determination of a strategyof deference or competition. If the interfering communication iscontinuous or unresponsive, then the only available strategy iscompetition. On the other hand, when the competing system uses, forexample, a CSMA system, such as 802.11, competition with such acommunication simply leads to retransmission, and therefore ultimatelyincreased network load, and a deference strategy may be more optimal, atleast and until it is determined that the competing communication isincessant. Other communications protocols, however, may have a more orless aggressive strategy. By observation of a system over time, itsstrategies may be revealed, and game theory permits composition of anoptimal strategy to deal with interference or coexistence. It is notedthat this strategy may be adopted adaptively by the entire ad hocnetwork, which may coordinate deference or competition as determinedoptimal.

The optimization process produces a representation of optimal networkarchitecture during the succeeding period. That is, value functionsrepresenting bids are broadcast, with the system then being permitted todetermine an optimal real valuation and distribution of that value.Thus, prior to completion of the optimization, potentially inconsistentallocations must be prevented, and each node must communicate itsevaluation of other node's value functions, so that the optimization isperformed on a normalized economic basis. This step may substantiallyincrease the system overhead, and is generally required for completionof the auction. This valuation may be inferred, however, forintermediate nodes in a multihop network path, since there is littlesubjectivity for nodes solely in this role, and the respective valuefunctions may be persistent. For example, the valuation applied by anode to forward information is generally independent of content andinvolved party.

A particular complication of a traffic information system is that thenature of the information held by any node is private to that node(before transmission), and therefore the valuation is not known untilafter all bids are evaluated. Thus, prior to completion of optimization,each node must communicate its evaluation of other nodes' valuefunctions, so that the optimization is performed on an economic basis.This required step substantially increases the system overhead. Thisvaluation may be inferred, however, for transit nodes in a multihopnetwork path.

As discussed above, may of the strategies for making the economicmarkets more efficient may be employed either directly, or analogy, tothe virtual economy of the ad hoc network. The ability of nodes to actas market maker and derivative market agents facilitates theoptimization, since a node may elect to undertake a responsibility(e.g., transaction risk), rather than relay it to others, and thereforethe control/administrative channel chain may be truncated at that point.If the network is dense, then a node which acts selfishly will bebypassed, and if the network is sparse, the node may well be entitled togain transactional profit by acting as a principal and trader, subjectto the fact that profits will generally be suboptimal if pricing is toohigh or too low.

After the network architecture is defined, compensation is paid to thosenodes providing value or subjected to a burden (including foregoingcommunication opportunity) by those gaining a benefit. The payment maybe a virtual currency, with no specific true value, and the virtualcurrency system provides a convenient method to flexibly tax, subsidize,or control the system, and thus steer the virtual currency to anormalized extrinsic value. In a real currency system, external controlsare more difficult, and may have unintended consequences. A hybrideconomy may be provided, linking both the virtual and real currencies,to some degree. This is especially useful if the network itselfinterfaces with an outside economy, such as the cellular telephonyinfrastructure (e.g., 2G, 2.5G, 3G, 4G, proposals for 5G, WiFi (802.11x)hotspots, WiMax (802.16x), etc.)

Using the protocol communication system, each node transmits its valuefunction (or change thereof), passes through communications fromneighboring nodes, and may, for example transmit payment information forthe immediate-past bid for incoming communications.

Messages are forwarded outward (avoiding redundant propagation back tothe source), with messages appended from the series of nodes.Propagation continues for a finite number of hops, until the entirecommunity has an estimate of the state and value function of each nodein the community. Advantageously, the network beyond a respectivecommunity may be modeled in simplified form, to provide a betterestimate of the network as a whole. If the propagation were notreasonably limited, the information would be stale by the time it isemployed, and the system latency would be inordinate. Of course, innetworks where a large number of hops are realistic, the limit may betime, distance, a counter or value decrement, or other variable, ratherthan hops. Likewise, the range may be adaptively determined, rather thanpredetermined, based on some criteria.

After propagation, each node evaluates the set of value functions forits community, with respect to its own information and ability toforward packets. Each node may then make an offer to supply or forwardinformation, based on the provided information. In the case of multihopcommunications, the offers are propagated to the remainder of thecommunity, for the maximum number of hops, including the originatingnode. At this point, each node has a representation of the state of itscommunity, with community edge estimates providing consistency for nodeswith differing community scopes, the valuation function each nodeassigns to control over portions of the network, as well as a resolvedvaluation of each node for supplying the need. Under thesecircumstances, each node may then evaluate an optimization for thenetwork architecture, and come to a conclusion consistent with that ofother members of its community. If supported, node reputation may beupdated based on past performance, and the reputation applied as afactor in the optimization and/or externally to the optimization. Asdiscussed above, a VCG-type auction is employed as a basis foroptimization. Since each node receives bid information from all othernodes within the maximum node count, the VCG auction produces anoptimized result.

As discussed above, by permitting futures, options, derivatives,insurance/indemnification/guaranties, long and short sales, etc., themarkets may be relatively stabilized as compared to a simple set ofindependent and sequential auctions, which may show increasedvolatility, oscillations, chaotic behavior, and other features which maybe inefficient.

Transmissions are preferably made in frames, with a single biddingprocess controlling multiple frames, for example a multiple of themaximum number of hops. Therefore, the bid encompasses a frame's-worthof control over the modalities. In the event that the simultaneous useof, or control over, a modality by various nodes is not inconsistent,then the value of the respective nodes may be summed, with the resultingallocation based on, for example, a ratio of the respective valuefunctions. As a part of the optimization, nodes are rewarded not onlyfor supporting the communication, but also for deferring their ownrespective communications needs. As a result, after controlling theresources, a node will be relatively less wealthy and less able tosubsequently control the resources, while other nodes will be more ableto control the resources. The distribution to deferred nodes also servesto prevent pure reciprocal communications, since the proposed mechanismdistributes and dilutes the wealth to deferring nodes.

Another possible transaction between nodes is a loan, that is, insteadof providing bandwidth per se, one node may loan a portion of itsgenerator function or accumulated wealth to another node. Presumably,there will be an associated interest payment. Since the currency in thepreferred embodiment is itself defined by an algorithm, the loantransaction may also be defined by an algorithm. While this concept issomewhat inconsistent with a virtual currency which declines in valueover time and/or space, it is not completely inconsistent, and, in fact,the exchange may arbitrage these factors, especially location-basedissues.

Because each node in the model presented above has complete information,for a range up to the maximum node count, the wealth of each node can beestimated by its neighbors, and payment inferred even if not actuallyconsummated. (Failure of payment can occur for a number of reasons,including both malicious and accidental). Because each hop addssignificant cost, the fact that nodes beyond the maximum hop distanceare essentially incommunicado is typically of little consequence; sinceit is very unlikely that a node more than 5 or 10 hops away will beefficiently directly included in any communication, due to theincreasing cost with distance, as well as reduction in reliability andincrease in latency. Thus, large area and scalable networks may exist.

Communications are generally of unencrypted data. Assuming the networkis highly loaded, this may allow a node to incidentally fulfill its datarequirements as a bystander, and thus at low cost meet its needs,allowing nodes with more urgent or directed needs to both control andcompensate the network. While this may reduce compensation tointermediaries and data sources, the improvements in efficiency willlikely benefit the network as a whole in increase stability, since weassume that peak load conditions will occur frequently.

Enforcement of responsibility may be provided by a centralized systemwhich assures that the transactions for each node are properly cleared,and that non-compliant nodes are either excluded from the network or atleast labeled. While an automated clearinghouse which periodicallyensures nodal compliance is preferred, a human discretion clearinghouse,for example presented as an arbitrator or tribunal, may be employed.

It is clear that, once an economic optimization methodology isimplemented, various factors may be included in the optimization, as setforth in the Summary and Objects of the invention and claims. Likewise,the optimization itself may have intrinsic limitations, which may createarbitrage opportunities. One set of embodiments of the present inventionencourages such arbitrage as a means for efficiently minimizingperturbations from optimality—as the model deviance from reality createslarger arbitrage opportunities, there will be a competitive incentivefor recruitment of agents as arbitragers, and also an incentive tocreate and implement better models. The resulting equilibrium may wellbe more efficient than either mechanism alone.

The Synthetic Economy

Exerting external economic influences on the system may have variouseffects on the optimization, and may exacerbate differences insubjective valuations. The application of a monetary value to thevirtual currency substantially also increases the possibility ofmisbehavior and external attacks. On the other hand, a virtual currencywith no assessed real value is self-normalizing, while monetizationleads to external and generally irrelevant influences as well aspossible external arbitrage (with potential positive and negativeeffects). External economic influences may also lead to benefits, whichare discussed in various publications on non-zero sum games.

In order to provide fairness, the virtual currency (similar to theso-called “nuglets” or “nugglets” proposed for use in the Terminodesproject) is self-generated at each node according to a schedule, anditself may have a time dependent value. L. Blazevic, L. Buttyan, S.Capkun, S. Giordiano, J.-P. Hubaux, and J.-Y. Le Boudec.Self-organization in mobile ad-hoc networks: the approach of terminodes.IEEE Communications Magazine, 39(6):166-174, June 2001; M. Jakobsson, J.P. Hubaux, and L. Buttyan. A micro-payment scheme encouragingcollaboration in multi-hop cellular networks. In Proceedings ofFinancial Crypto 2003, January 2003; J. P. Hubaux, et al., “TowardSelf-Organized Mobile Ad Hoc Networks: The Terminodes Project”, IEEECommunications, 39(1), 2001. citeseer.ist.psu.edu/hubaux01toward.html;Buttyan, L., and Hubaux, J.-P. Stimulating Cooperation inSelf-Organizing Mobile Ad Hoc Networks. Tech. Rep.DSC/citeseer.ist.psu.edu/buttyan01stimulating.html; Levente Buttyan andJean-Pierre Hubaux, “Enforcing Service Availability in Mobile Ad-HocWANs”, 1st IEEE/ACM Workshop on Mobile Ad Hoc Networking and Computing(MobiHOC citeseer.ist.psu.edu/buttyan00enforcing.html; L. Buttyan andJ.-P. Hubaux. Nuglets: a virtual currency to stimulate cooperation inself-organized ad hoc networks. Technical Report DSC/2001,citeseer.ist.psu.edu/article/buttyan01nuglets.html; Mario Cagalj,Jean-Pierre Hubaux, and Christian Enz. Minimum-energy broadcast inall-wireless networks: Np-completeness and distribution issues. In TheEighth ACM International Conference on Mobile Computing and Networking(MobiCom 2002), citeseer.ist.psu.edu/cagalj02minimumenergy.html; N. BenSalem, L. Buttyan, J. P. Hubaux, and Jakobsson M. A charging andrewarding scheme for packet forwarding. In Proceeding of Mobihoc, June2003. For example, the virtual currency may have a half-life ortemporally declining value. On the other hand, the value may peak at atime after generation, which would encourage deference and short termsavings, rather than immediate spending, and would allow a recipientnode to benefit from virtual currency transferred before its peak value.This also means that long term hoarding of the currency is of littlevalue, since it will eventually decay in value, while the systempresupposes a nominal rate of spending, which is normalized among nodes.The variation function may also be adaptive, but this poses asynchronization issue for the network. An external estimate of nodewealth may be used to infer counterfeiting, theft and failure to paydebts, and to further effect remediation.

The currency is generated and verified in accordance with micropaymenttheory. Rivest, R. L., A. Shamir, PayWord and MicroMint: Two simplemicropayment schemes, also presented at the RSA '96 conference,http//theory.lcs.mit.edu/rivest/RivestShamirmpay.ps,citeseer.ist.psu.edu/rivest96payword.html; Silvio Micali and RonaldRivest. Micropayments revisited. In Bart Preneel, editor, Progress inCryptology—CT-RSA 2002, volume 2271 of Lecture Notes in ComputerScience. Springer-Verlag, Feb. 18-22, 2002.citeseer.ist.psu.edu/micali02micropayments.html.

Micropayment theory generally encompasses the transfer of secure tokens(e.g., cryptographically endorsed information) having presumed value,which are intended for verification, if at all, in a non-real timetransaction, after the transfer to the recipient. The currency iscirculated (until expiration) as a token, and therefore may not besubject to immediate definitive authentication by source. Since thesetokens may be communicated through an insecure network, the issue offorcing allocation of payment to particular nodes may be dealt with bycryptographic techniques, in particular public key cryptography, inwhich the currency is placed in a cryptographic “envelope” (cryptolope)addressed to the intended recipient, e.g., is encrypted with therecipient's public key, which must be broadcast and used as, or inconjunction with, a node identifier. This makes the payment unavailableto other than the intended recipient. The issue of holding the encryptedtoken hostage and extorting a portion of the value to forward the packetcan be dealt with by community pressure, that is, any node presentingthis (or other undesirable) behavior might be ostracized. The likelihoodof this type of misbehavior is also diminished by avoiding monetizationof the virtual currency. Further, redundant routing of such informationmay prevent single-node control over such communications.

This currency generation and allocation mechanism generally encouragesequal consumption by the various nodes over the long term. In order todiscourage excess consumption of bandwidth, an external tax may beimposed on the system, that is, withdrawing value from the system basedon usage. Clearly, the effects of such a tax must be carefully weighed,since this will also impose an impediment to adoption as compared to anuntaxed system. On the other hand, a similar effect use-disincentive maybe obtained by rewarding low consumption, for example by allocating anadvertising subsidy between nodes, or in reward of deference. Theexternal tax, if associated with efficiency-promoting regulation, mayhave a neutral or even beneficial effect.

Each node computes a value function, based on its own knowledge state,risk profile and risk tolerance, and wealth, describing the value to itof additional information, as well as its own value for participating inthe communications of others. The value function typically includes apast travel history, future travel itinerary, present location, recentcommunication partners, and an estimator of information strength andweakness with respect to the future itinerary. It may be presumed thateach node has a standard complement of sensors, and accurately acquireddescriptive data for its past travel path. Otherwise, a description ofthe available information is required. One advantage of a value functionis that it changes little over time, unless a need is satisfied orcircumstances change, and therefore may be a persistent attribute. Usingthe protocol communication system, each node transmits its valuefunction (or change thereof), passes through communications fromneighboring nodes, and may, for example transmit payment information forthe immediate-past bid for incoming communications.

Messages are forwarded outward (avoiding redundant propagation back tothe source), with messages appended from the series of nodes.Propagation continues for a finite number of hops, until the entirecommunity has an estimate of the state and value function of each nodein the community. Advantageously, the network beyond a respectivecommunity may be modeled in simplified form, to provide a betterestimate of the network as a whole.

After propagation, each node evaluates the set of value functions forits community, with respect to its own information and ability toforward packets. Each node may then make an offer to supply or forwardinformation, based on the provided information. In the case of multihopcommunications, the offers are propagated to the remainder of thecommunity, for the maximum number of hops, including the originatingnode. At this point, each node has a representation of the state of itscommunity, with community edge estimates providing consistency for nodeswith differing community scopes, the valuation function each nodeassigns to control over portions of the network, as well as a resolvedvaluation of each node for supplying the need. Under thesecircumstances, each node may then evaluate an optimization for thenetwork architecture, and come to a conclusion consistent with that ofother members of its community. If supported, node reputation may beupdated based on past performance, and the reputation applied as afactor in the optimization and/or externally to the optimization. Asdiscussed above, a VCG-type auction is employed as a basis foroptimization. Since each node receives bid information from all othernodes within the maximum node count, the VCG auction produces anoptimized result.

Transmissions are made in frames, with a single bidding processcontrolling multiple frames, for example a multiple of the maximumnumber of hops. Therefore, the bid encompasses a frame's-worth ofcontrol over the modalities. In the event that the simultaneous use of,or control over, a modality by various nodes is not inconsistent, thenthe value of the respective nodes may be summed, with the resultingallocation based on, for example, a ratio of the respective valuefunctions. As a part of the optimization, nodes are rewarded not onlyfor supporting the communication, but also for deferring their ownrespective needs. As a result, after controlling the resources, a nodewill be relatively less wealthy and less able to subsequently controlthe resources, while other nodes will be more able to control theresources. The distribution to deferred nodes also serves to preventpure reciprocal communications, since the proposed mechanism distributesand dilutes the wealth to deferring nodes.

Because each node in the model presented above has complete information,for a range up to the maximum node count, the wealth of each node can beestimated by its neighbors, and payment inferred even if not actuallyconsummated. (Failure of payment can occur for a number of reasons,including both malicious and accidental). Because each hop addssignificant cost, the fact that nodes beyond the maximum hop distanceare essentially incommunicado is typically of little consequence; sinceit is very unlikely that a node more than 5 or 10 hops away will beefficiently included in any communication, due to the increasing costwith distance, as well as reduction in reliability and increase inlatency. Thus, large area and scalable networks may exist.

Typically, cryptography is employed for both authentication and topreserve privacy. External regulation, in a legal sense at least, istypically imposed by restrictions on hardware and software design, aswell as voluntary compliance at risk of detection and legal sanction.

A synthetic economy affords the opportunity to provide particularcontrol over the generator function, which in turn supports a hierarchy.In this scheme, each node controls the generator function atrespectively lower nodes, and thus can allocate wealth amongsubordinates. If one assumes real time communications, then it is clearthat the superordinate node can directly place bids on behalf ofsubordinates, thus effectively controlling its entire branch. In theabsence of real time communications, the superordinate node must deferto the discretion of the subordinate, subject to reallocation later ifthe subordinate defects. If communications are impaired, and a set of apriori instructions are insufficient, then it is up to the subjectiveresponse of a node to provide deference.

It is noted that when sets of nodes “play favorites”, the VCG auctionwill no longer be considered “strategyproof”. The result is that bidderswill assume bidding strategies that do not express their secretvaluation, with the result being likely suboptimal market finding duringthe auction. This factor can be avoided if hierarchal overrides andgroup bidding play only a small role in the economy, and thus theexpected benefits from shaded bidding are outweighed by the normaloperation of the system. For example, by taxing transactions,over-valued bidding will be disincentivized, and by redistributingeconomic surplus to bystanders, the aggregate wealth of the controllinggroup will be mitigated.

A synthetic economy affords the opportunity to provide particularcontrol over the generator function, which in turn provides particularadvantages with respect to a hierarchal organization. In this scheme,each node has the ability to control the generator function atrespectively lower nodes, and thus can allocate wealth amongsubordinates. If one assumes real time communications, then it is clearthat the superordinate node can directly place bids on behalf ofsubordinates, thus effectively controlling its entire branch. In theabsence of real time communications, the superordinate node must deferto the discretion of the subordinate, subject to reallocation later ifthe subordinate defects. If communications are impaired, and a set of apriori instructions are insufficient, then it is up to the subjectiveresponse of a node to provide deference. Thus, a node may transfer allor a portion of its generator function, either for a limited time orpermanently, using feed-forward or feedback control. In this sense, thehierarchal and financial derivatives, options, futures, loans, etc.embodiments of the invention share a common theme.

It is noted that when sets of nodes “play favorites”, the VCG auctionwill no longer be considered “strategyproof”. The result is that bidderswill assume bidding strategies that do not express their secretvaluation, with the result being likely suboptimal market price findingduring the auction. This factor can be avoided if hierarchal overridesand group bidding play only a small role in the economy, and thus theexpected benefits from shaded bidding are outweighed by the normaloperation of the system. On the other hand, the present inventionpotentially promotes competition within branches of a hierarchy, to theextent the hierarchy does not prohibit this. Between different branchesof a hierarchy, there will generally be full competition, while withincommonly controlled branches of a hierarchy, cooperation will beexpected. Since the competitive result is generally more efficient,there will be incentive for the hierarchal control to permit competitionas a default state, asserting control only where required for thehierarchal purpose.

Military Hierarchy

In a typical auction, each player is treated fairly; that is, the samerules apply to each player, and therefore a single economy describes theprocess. The fair auction therefore poses challenges for an inherentlyhierarchal set of users, such as a military organization. In themilitary, there is typically an expectation that “rank has itsprivileges”. The net result, however, is a decided subjective unfairnessto lower ranking nodes. In a mobile ad hoc network, a real issue is userdefection or non-compliance. For example, where a cost is imposed on auser for participating in the ad hoc network, e.g., battery powerconsumption, if the anticipated benefit does not exceed the cost, theuser will simply turn off the device until actually needed, to conservebattery power outside the control of the network. The result of massdefection will of course be the instability and failure of the ad hocnetwork itself. Thus, perceived fairness and net benefit is required toimportant for network success, assuming that defection or non-complianceremains possible.

On the other hand, in military systems, the assertion of rank as a basisfor priority is not necessarily perceived as arbitrary and capricious,and is generally not perceived subjectively as such. Orders andcommunications from a central command are critical for the organizationitself. Therefore, the difficulty in analyzing the application of a fairgame to a hierarchal organization is principally a result ofconceptualizing and aligning the individual incentives with those of theorganization as a whole. Since the organization exists outside of the adhoc network, it is generally not unrealistic to expect compliance withthe hierarchal attributes both within and outside of the network.

An artificial economy provides a basis for an economically efficientsolution. In this economy, each node has a generator function forgenerating economic units which are used in a combinatorial auction withother nodes. The economic units may have a declining value, so thatwealth does not accumulate over long periods, and by implication, wealthaccumulated in one region is not available for transfer in a distantregion, since the transfer may be subject to latency and/or cost. Evenif a low latency system is employed to transfer the value, an expressspatially declining value function may also be imposed. The geographicdecline may also be explicit, for example based on a GPS or navigationalsystem. In other cases, nodal motility is valuable, and mobile nodes areto be rewarded over those which are stationary. Therefore, the value ora portion thereof, or the generator function, may increase with respectto relocations.

This scheme may be extended to the hierarchal case by treating eachchain of command as an economic unit with respect to the generatorfunction. At any level of the hierarchy, the commander retains a portionof the wealth generation capacity, and delegates the remainder to itssubordinates. In the case of real-time communications, a commander maydirectly control allocation of the generator function at each timeperiod. Typically, there is no real-time communications capability, andthe wealth generator function must be allocated a priori. Likewise,wealth may also be reallocated, although a penalty is incurred in theevent of an initial misallocation since the transfer itself incurs acost, and there will be an economic competitive distortion, under whicha node's subjective value of a resource is influenced by its subjectivewealth. If a node is supplied with wealth beyond its needs, the wealthis wasted, since it declines in value and cannot be hoardedindefinitely. If a node is supplied with insufficient wealth, economicsurplus through transactional gains are lost. Thus, each node mustanalyze its expected circumstances to retain or delegate the generatorfunction, and to optimally allocate wealth between competingsubordinates.

In any transaction, there will be a component which represents thecompetitive “cost”, and a possible redistribution among nodes within ahierarchal chain. This redistribution may be of accumulated wealth, orof the generation function portion. In the former case, if thecommunication path fails, no further transfers are possible, while inthe later case, the result is persistent until the transfer functionallocation is reversed. It is also possible to transfer an expiring ordeclining portion of the generating function; however, this might lead anode which is out of range to have no ability to rejoin the network uponreturn, and thus act as an impediment to efficient network operation. Asdiscussed above, one possibility is for nodes to borrow or loadcurrency. In this case, a node deemed credit-worthy may blunt the impactof initially having insufficient wealth by merely incurring atransaction cost (including interest, if applied).

In practice, the bulk of the wealth generating function will be widelydistributed, and not concentrated at the top of the hierarchy. If thisis true, under most circumstances, the network will appear to operateaccording to a non-hierarchal or fair VCG model, but in somecircumstances, normal operation may be usurped by nodes which haveapparent excess wealth resulting from a superior wealth generatorfunction. Typically, hierarchically superior nodes will use theirability to transfer wealth to themselves, or to recruit subordinates tocooperate, in order to directly or indirectly control the networkresources. It is possible, however, for nodes within one branch of ahierarchy to conspire against nodes outside that branch, resulting in adifferent type of distortion. Since the ad hoc network typically gainsby having a larger number of participating nodes, this type of behaviormay naturally be discouraged. On the other hand, hierarchically superiornodes either retain, or more likely, can quickly recruit surroundingsubordinates to allocate their wealth generating function andaccumulated wealth to pass urgent or valuable messages.

Where expensive assets are employed, an actual transfer of wealth or thegenerator function to a single entity may be required. For example, ahigh level node might have access to a high power broadcast system,which interferes with other communications, or simply incurs a high costto operate. Low level nodes might ordinarily be limited to cellular(i.e., short range, low power radio) wireless communications. In orderfor a low level node to control an expensive asset, the assent orcooperation of others may be required, for example by hierarchalsuperiors.

Since the network should be stable in the absence of command and controlcommunications, a hierarchal superior should assure that subordinatenodes possess sufficient wealth and motivation to maintain ad hocnetwork operation. Insufficient wealth will tend to eliminate theadvantage to nodal participation (and therefore encourage defection),unless payments from acting as intermediary are significant. Thus, anode with insufficient wealth generation function may potentiallyexhaust its resources, and be unavailable for ad hoc intermediary use,even for the benefit of the hierarchy. On the other hand, an initialallocation of too much wealth will encourage high spending and lessactive participation as an intermediary. While it is possible in amilitary system to formulate an “engineered” solution which forcesparticipation and eliminates defection, this solution does not gain thebenefit of economic optimization and may have limited applicationoutside of mandatory hierarchies.

Game theory is a useful basis for analyzing ad hoc networks, andunderstanding the behavior of complex networks of independent nodes. Bypresuming a degree of choice and decision-making by nodes, we obtain ananalysis that is robust with respect to such considerations. Theprincipal issues impeding deployment are the inherent complexity of thesystem, as well as the overhead required to continuously optimize thesystem. Determination of a set of simplifying presumptions to reduceprotocol overhead and reduce complexity may improve performance.Hierarchal considerations can be imposed to alter the optimization ofthe system, which would be expected to provide only a small perturbationto the efficient and optimal operation of the system according to a pureVCG protocol. A marketplace auction with competition between potentialbuyers and potential sellers, and with the economic surplus distributedbetween parties which must defer to active participants, providesincentive to all affected parties, and therefore may provide a betterresult than a simple transfer between supply and demand elements only.

The ad hoc network does not exist in a vacuum. There are variouscompeting interests seeking to use the same bandwidth, and technologicalsuperiority alone does not assure dominance and commercial success. Gametheory may also be used as a tool to analyze the entities which seek todeploy ad hoc networks, especially where they compete.

First Embodiment

In a typical auction, each player is treated fairly; that is, the samerules apply to each player, and therefore a single economy describes theprocess. The fair auction therefore poses challenges for an inherentlyhierarchal set of users, such as a military organization, where rank isaccompanied by privilege. The net result, however, is a decided apparentdisadvantage to lower ranking agents, at least when viewed in light ofconstricted self-interest. The issues that arise are similar to therelating to “altruism”, although not identical, and thus the gametheoretic analysis of altruistic behavior may be imported forconsideration, as appropriate.

In a mobile ad hoc communications network, a real issue is userdefection or non-compliance. For example, where a cost is imposed on auser for participating in the ad hoc network, e.g., battery powerconsumption in a mesh radio network, if the anticipated benefit does notexceed the cost, the user will simply turn off or disable the deviceuntil actually needed. The result of mass defection will, of course, bethe instability and failure of the ad hoc network itself, leading todecreased utility, even for those who gain an unfair or undue advantageunder the system. Thus, perceived fairness and net benefit is requiredfor network success, assuming that defection and/or non-compliance arepossible.

On the other hand, in military systems, the assertion of rank as a basisfor priority is not itself necessarily arbitrary or capricious. Ordersand communications from a central command are critical for theorganization itself, and thus the lower ranking agents gain at least aperipheral, if not direct benefit as their own chain of command employstheir resources. Therefore, the difficulty in analyzing the applicationof a fair game paradigm to a hierarchal organization is principally aresult of conceptualizing and aligning the individual incentives withthose of the organization as a whole and the relationship betweenbranches. Thus, in contradistinction to typical self-organizingpeer-to-peer networks, a hierarchal network is not seen asself-organizing, at least in terms of the hierarchy, which is extrinsicto the formation of the communications network under consideration.

As discussed below, the “distortions” of the network imposed by theexternal hierarchy can be analyzed and accounted for by, for example,the concepts of inheritance and delegation. Thus, each branch of ahierarchy tree may be considered an object, which receives a set ofcharacteristics from its root, and from which each sub-branch inheritsthe characteristics and adds subcharacteristics of, for example,specialization. It is noted that the hierarchy need not follownon-ambiguous or perfect rules, and thus there is no particular limitimposed that the hierarchy necessarily follow these formalisms. Rather,by analyzing those aspects of the hierarchy which comply with theseformalisms in accordance therewith, efficiency is facilitated.

In establishing an economic system, a preliminary question is whetherthe system is microeconomic or macroeconomic; that is, whether theeconomy is linked to a real economy or insulated from it. Onedisadvantage of a real economy with respect to a peer relationship isthat external wealth can override internal dynamics, thus diminishingthe advantages to be gained by optimization, and potentially creating aperception of unfairness for externally less wealthy agents, at leastunless and until the system accomplishes a wealth redistribution. Anartificial economy provides a solution for a peer network in which eachnode has an equal opportunity to gain control over the ad hoc network,independent of outside influences and constraints. On the other hand, byinsulating the network from external wealth redistribution, realefficiency gains may be unavailable. Therefore, both types of economies,as well as hybrids, are available. Thus, as discussed in more detailbelow, a “fair” initial (or recurring) wealth distribution may beapplied, which may be supplemented with, and/or provide an output of,external wealth. The rules or proportion of external influence may bepredetermined, adaptive, or otherwise.

In accordance with the proposed artificial economy, each node has agenerator function for generating economic units, which are then used ina transaction (e.g., an auction) with other nodes to create a marketeconomy, that is, each node has a supply and demand function, and actsas a source or sink for a limited resource. In some cases, nodes mayhave only supply or demand functions, or a degree of asymmetry, but inthis case, these are typically subject to an external economicconsideration, and the artificial economy will be less effective inproviding appropriate incentives. According to one implementation ofthis embodiment, the artificial economic units have a temporally and/orspatially declining value, so that wealth does not accumulate over longperiods and/or cannot be transferred over large distances. The declinemay be linear, exponential, or based on some other function. Thiscreates a set of microeconomies insulated from each other. Where distantmicroeconomies must deal with each other, there is a discount. Thisarchitecture provides a number of advantages, for example, by decreasingthe influence of more spatially and temporally distant effects, thescope of an optimization analysis may be relatively constrained, whilereducing the amount of information which must be stored over time and/orcarried over distance in order to permit an optimization. Likewise,since the economy is artificial, the discount need not be recoupedwithin the scope of the system; that is, conservation of capital is notrequired. In the same manner, a somewhat different incentive structuremay be provided; that is, economic units generated at one location andat one time may have a higher value at a different location and time;this may encourage reduced immediate use of the system resources, andrelocation to higher valued locations. As discussed below, oneembodiment of the invention permits trading of credits, and thus, forexample, a user may establish a repeater site at an under-servedlocation to gain credits for use elsewhere. Preferably, beyond a “nearfield” effect, the value does not continue to increase, since this mayresult in inflationary pressures, and undermine the utility of thesystem in optimally balancing immediate supply and demand at aparticular location.

As can be seen, through modifications of the governing rules andformulae, the system can be incentivized to behave in certain ways, butcare should be exercised since a too narrow analysis of the incentivemight result in unintended long term or distant effects. To the extentthat human behavior and subjective analysis is involved, care shouldalso be exercised in applying a rationality assumption, since this isnot always true. Rather, there may be applicable models for humanirrational behavior that are better suited to an understanding of thenetwork behavior in response to a perturbation.

The typical peer-to-peer ad hoc network may be extended to thehierarchal case by treating each branch (including sub-branches) withinthe chain of command as an economic unit with respect to the generatorfunction. At any level of the hierarchy, the commander optionallyretains a portion of the wealth generation capacity, and delegates theremainder to its subordinates. Therefore, the rank and hierarchalconsiderations are translated to an economic wealth (or wealthgeneration) distribution. One aspect of this system allows wealthtransfer or redistribution, although in a real system, a time delay isimposed, and in the event of a temporally and/or spatially decliningvalue, the transfer will impose a cost. Thus, an initial misallocationis undesired, and there will be an incentive to optimally distribute thewealth initially. Of course, if centralized control with low penalty isdesired, it is possible to limit the penalty, if any, for wealthredistribution through appropriate rules, although the time forpropagation through the network remains an issue, and blind nodes (i.e.,those which do not have an efficient communication path, or haveinsufficient resources to utilize otherwise available paths through thehierarchy) may also lead to limitations on system performance.

In this system, there may be an economic competitive distortion, underwhich a node's subjective value of a resource is influenced by its thensubjective wealth. If a node is supplied with wealth beyond its needs,the wealth is wasted, since it may decline in value and cannot behoarded indefinitely. (In a network wealth model in which wealth couldbe hoarded indefinitely, small deviations from optimality and arbitrageopportunities may be exploited to create a perception of unfairness,thus, this is not preferred.) If a node is supplied with insufficientwealth, economic surplus through transactional gains are lost. Thus,each node must analyze its expected circumstances to retain or delegatethe generator function, and to optimally allocate wealth betweencompeting subordinates. Likewise, there may be a plurality ofquasi-optimal states.

In any economic transaction, there is an amount that a seller requiresto part with the resource, a price a buyer is willing to pay, and asurplus between them. Typically, in a two party transaction, the surplusis allocated to the party initiating the transaction, that is, the partyinitiating the transaction uses some discovery mechanism to find theminimum price acceptable by the buyer. In brokered or agent-mediatedtransactions, a portion of the surplus is allocated to a facilitator.

In accordance with one aspect of the present invention, compliance withthe community rules, as well as an incentive to bid or ask a trueprivate value is encouraged by distributing a portion of the transactionsurplus to losing competitive bidders. While according to one proposal,this portion is allocated in accordance with their reported valuations,this creates a potential incentive for bidders who know they will not bewinning bidders to overbid, and thereby gain an increased portion of thesurplus. In order to reward honest reporting of private values, thereward function must penalize both overreporting and underreporting ofprivate values. This circumstance occurs if, at each bid, there is arisk of winning commensurate with the bid, and thus the system isstrategyproof. In order to achieve this circumstance, for example, astatistical noise or probability distribution may be added to thesystem, with an acceptance of a bid made a statistical process. Thisresults in a “fuzzy” boundary on the bid value, although it may imposean inefficiency on the market since any deviation from the optimalmarket price represents a loss.

Another approach to minimizing strategic bidding is to impose a bid fee.That is, each bidder must offer a prepayment corresponding to a smallportion of its bid, thereby disincentivizing bidding to lose. Thewinning bidder will then pay a second price plus the deposit bid. Thesellers will receive their own lowest cost (or second cost) bid. Losingbidders will receive a payment in accordance with the value of theirbid, less the bid deposit. In order to disincentivize strategic bidding,the average return to a bidder is less than the bid cost. In fact, agood target for the bidder deposit is the administrative cost oftransacting the bidding negotiations. This, in turn, provides anincentive to keep the administrative overhead low, thus improvingoverall system performance, especially where the administrativecommunications compete with normal communications for bandwidth. In thiscircumstance, those bidding to win receive either the benefit of thetransaction or a payment for deference, less the transactional fee.Those who are bidding strategically, in manner seeking to acquire thedeference payment, must risk the transactional cost, and to gainsubstantially, must submit a relatively high bid. When the bids are“competitive”, there is a substantial risk that the bid will be awinning bid, and thus incur the full bid cost. Thus, there is adisincentive to bidding a high value, but without an intent to win. Ofcourse, the bid deposit may be a flat fee, or subject to a mathematicalor adaptive function, rather than directly related to administrativecost.

The aggregated bid deposits may, for example, be awarded to a class whoare optimally incentivized by the nature of this payment. For example,it may be awarded to those selling bandwidth, in a manner generallyinversely proportional to the value of their ask, or, for example, basedon allocations during the combinatorial (VCG) auction. This paymentwould then incentivize sellers to offer services at a low price,improving network availability.

Of course, there may be other classes within the auction population whomay be taxed or subsidized, using value derived from the auctionprocess.

In a strategyless auction, automated bidding is quite feasible, sincethe optimal bid is the computed value. For auctions in which a bidderdoes not have an incentive to bid its true private value, and this mustassume a strategic play, automated bidding becomes more of a challenge,but may also be automated.

In a strategy-less auction, a bidder cannot gain by bidding over orunder its private value. If a bidder bids below its private value, ithas a reduced chance of gaining the benefit of the transaction.

In an auction which is subject to strategic bidding, the strategy may bemitigated by imposing commensurate risks and costs to balance theperceived advantage toward zero.

In particular, the competitive bidders seeking to allocate a scarceresource for themselves receive compensation for deferring to thewinning bidder in an amount commensurate with their reported value.Thus, sellers receive their minimum acceptable value, buyers pay theirmaximum valuation, the surplus is distributed to the community in amanner tending to promote the highest bids within the private value ofthe bidder. In a corresponding manner, the auction rules can beestablished to incentivized sellers to ask the minimum possible amount,above their reserve. For example, a portion of the surplus may beallocated to bidders in accordance with how close they come to thewinning ask. Therefore, both incentives may be applied, for example withthe surplus split in two, and half allocated to the bidder pool and halfallocated to the seller pool. Clearly, other allocations orproportionations are possible.

The winning bidder and/or seller may be included within the rebate pool.This is particularly advantageous where for various reasons, the winningbidder is not selected. Thus, this process potentially decouples thebidding (auction) process and the resulting commercial transaction.

Because of transactional inefficiencies, human behavioral aspects, and adesire to avoid increased network overhead by “false” bidders seeking ashare of the allocation pool without intending to win the auction, itmay be useful to limit the allocation of the surplus pool to a subset ofthe bidders and/or sellers, for example the top three of one or both.This therefore encourages bidders and/or sellers to seek to be in thelimited group splitting the pool, and thus incentivizes higher bids andlower asks. Of course, a party will have a much stronger incentive toavoid bidding outside its valuation bounds, so the risk of this type ofinefficiency is small.

As discussed above, one embodiment of the invention provides a possibleredistribution or wealth among nodes within a hierarchal chain. Thisredistribution may be of accumulated wealth, or of the generationfunction portion. Trading among hierarchally related parties ispreferred, since the perceived cost is low, and the wealth can berepeatedly redistributed. In fact, it is because of the possibility ofwealth oscillation and teaming that the declining wealth function ispreferred, since this will tend to defeat closely related party controlover the network for extended periods.

It is noted that, in a multihop mobile ad hoc network, if acommunication path fails, no further transfers are possible, potentiallyresulting in stalled or corrupt system configuration. It is possible totransfer an expiring or declining portion of the generating function;however, this might lead a node which is out of range to have no abilityto rejoin the network upon return, and thus act as an impediment toefficient network operation. Therefore, it is preferred that, in anartificial economy, each node has some intrinsic wealth generatorfunction, so an extended period of inactivity, a node gains wealthlikely sufficient to rejoin the network as a full participant.

In practice, in a typical military-type hierarchy, the bulk of thewealth generating function will be distributed to the lowest ranks withthe highest numbers. Thus, under normal circumstances, the network willappear to operate according to a non-hierarchal (i.e., peer) model, withthe distortion that not all nodes have a common generator function. Onthe other hand, hierarchically superior nodes either retain, or morelikely, can quickly recruit surrounding subordinates to allocate theirwealth generating function and accumulated wealth to pass urgent orvaluable messages. Thus, if 85% of the wealth and network resources aredistributed to the lowest-ranking members, then the maximum distortiondue to hierarchal modifications is 15%.

One way that this allocation of wealth may be apparent is with respectto the use of expensive assets. Thus, a high level node might haveaccess to a high power broadcast system or licensed spectrum, while lowlevel nodes might ordinarily be limited to lower power transmissionand/or unlicensed spectrum or cellular wireless communications. For alow level node to generate a broadcast using an expensive asset (or toallocate a massive amount of space bandwidth product), it must pass therequest up through the chain of command, until sufficient wealth (i.e.,authority) is available to implement the broadcast.

In fact, such communications and authorizations are quite consistentwith the expectations within a hierarchal organization, and thisconstruct is likely to be accepted within a military-type hierarchalorganization.

Under normal circumstances, a superior would have an incentive to assurethat each subordinate node possesses sufficient wealth to carry out itsfunction and be incentivized to participate in the network. If asubordinate has insufficient initial wealth (or wealth generatingfunction) allocation, it may still participate, but it must expend itsinternal resources to obtain wealth for participation toward its ownbenefit. This, in turn, leads to a potential exhaustion of resources,and the unavailability of the node for ad hoc intermediary use, even forthe benefit of the hierarchy. An initial surplus allocation will lead tooverbidding for resources, and thus inefficient resource allocation,potential waste of allocation, and a disincentive to act as anintermediary in the ad hoc network. While in a traditional militaryhierarchy, cooperation can be mandated, in systems where cooperation isperceived as contrary to the net personal interests of the actor,network stability may be poor, and defection in spite of mandate.

In a military system, it is thus possible to formulate an “engineered”solution which forces participation and eliminates defection; however,it is clear that such solutions forfeit the potential gains ofoptimality, and incentivizes circumvention and non-compliance. Further,because such a system is not “cost sensitive” (however the appropriatecost function might be expressed), it fails to respond to “market”forces.

Accordingly, a peer to peer mobile ad hoc network suitable forrespecting hierarchal organization structures is provided. In thishierarchal system, the hierarchy is represented by an initial wealth orwealth generation function distribution, and the hierarchally highernodes can reallocate wealth of nodes beneath themselves, exercisingtheir higher authority. This wealth redistribution can be overt orcovert, and if overt, the hierarchal orders can be imposed without nodalassent. In a covert redistribution, trust may be required to assureredistribution by a node to a grandchild node.

The wealth and its distribution can be implemented using modifiedmicropayment techniques and other verifiable cryptographic techniques.This wealth can be applied to auctions and markets, to allocateresources. Various aspects of this system are discussed in more detailelsewhere in this specification.

In accordance with aspects of this embodiment, an example is provided.In this scenario, a vehicle traveling along a highway seeks trafficinformation 10-20 miles ahead on the road. The transceiver in thevehicle has a range of about 0.5 miles, meaning that, assuming maximumhop range, 20-40 hope would be necessary in each direction in order tofulfill a response to a request for information. If we further assumethat the traffic density allows an average density of compatibletransceivers of 1 per 0.05 miles², then it would appear that for eachhop, a number of intermediaries would be possible. We further assumethat each vehicle has a pair of antennas (which may operate on differentfrequencies), forward and backward looking, so that forward And backwardcommunications are non-interfering. It is noted that, in operation, itis not a single vehicle that seeks information responding to a request;rather, it is likely that 2-25% of vehicles will seek information withina short period, especially of the cost of fulfilling a request isrelatively low. We also assume that there is no common trigger event,such as an accident, which would provoke essentially all vehicles torequest the same information, a circumstances that could be addressedthrough a multicast or broadcast.

If the vehicle sought to arrange a communication over the entire 10-20miles in advance of communicating, this would require a multifactorialoptimization likely involving over 100 transceivers, and if even one ofthe 20-40 intermediates fails, the entire communication fails. Theadministrative overhead for this process may not outweigh itsadvantages.

On the other hand, if we instead presume that the vehicle only optimizea path over a limited range or number of hops, e.g., 1 mile or 5 hops,then the optimization is facilitated and the administrative overheadreduced. On the other hand, this requires that vehicles or nodes at thefringe arrange for completion of the communication. It is here that thestatistical aspects of the network architecture may be exploited toachieve efficiencies. Thus, in observing or participating in the networkactivities over a period of time, a node can model the behavior ofnearby nodes, and determine a degree of risk with respect to the model.That node may then undertake the risk associated with its assessment ofits environment, and communicate an offer to act as agent for completionof the communication, without explicitly communicating the details ofthe communication. Therefore, the originating node optimizes a localregion ad hoc network, and then adopts an estimate of the network statebeyond the edge of the local region.

Economically, the vehicle seeking the information broadcasts a bid orvalue function of its valuation of the resources it requires. This bidis propagated to the local region or beyond, and compared with the bidsor value functions of other vehicles or nodes. A winning vehicle or nodethen assumes control over the minimum temporal-spatial-frequency channelrequired. As stated above, at the edge of the local region, nodes mayact as proxies or agents, and undertake the risk of the more distantcommunication, adding a risk premium to their ask. The node with thelowest ask is selected as the agent or proxy. It is noted that the roleof communication intermediary and proxy or agent is discrete, andtherefore need not be a single element, though certain efficiencies aregained if this is the case. The agent or proxy must also conduct anauction for the next stage of the communication, in a process which isrepeated until the destination node is included within the local region.

The proxy or agent undertakes the risk of the cost of the downstreamcommunications, as well as the risk of non-payment, and thus may wellcharge a substantial premium over its actual risk-free cost. Therefore,the efficiency gained through the use of the agent or proxy derives fromthe administrative efficiencies gained, as well as comprehension thatthe risks are inherent, and must generally be undertaken by some elementof the network. The incrementally added risks may be small, but aretransferred. A node which promotes itself for acting as agent or proxymay do so because it has lower risks, costs or otherwise unproductiveassets. For example, a cellular telephone carrier may choose toparticipate in the network, using its fixed infrastructure as a backup,or bypass. In that case, if the network fails, or is less efficient, ithas the option of using its own facilities.

The agent or proxy therefore arbitrages the risk, based on its ownknowledge of its local region which is different from the local regionof the originator of the communication. There may be less competitionfor the role of arbitrageur, allowing it to claim a larger portion ofthe economic surplus. In fact, an arbitrageur may pre-acquire resourcesat a defined price, and resell them later at a profit. Thus, it is seenthat economic efficiencies and increased profits for intermediaries arenot inconsistent, where opportunities for reduction in inefficienciesexist.

Adding hierarchal element to this example, it is noted that certainrisks are reduced when transactions are conducted between relatedentities. For example, if their respective wealth is interlinked, overthe long term, the risk of non-payment is abated. Likewise, the risk ofdefection or non-compliance is reduced. Further, since it is presumedthat the benefit function of related nodes is intertwined, actual costsmay be reduced, since the communication itself is a countervailingbenefit to the cost of a related node conveying the message or packet.Thus, there will likely be a preference for communications between moreclosely related nodes than between more distantly related or unrelatednodes. On the other hand, since wealth (virtual or real) itself isdesirable, and inter-party transactions limit wealth gain opportunities,there will also be an incentive to conduct transactions with unrelatednodes for full value. As discussed above, in a hierarchy, a top levelnode is initially allocated the entire wealth and/or wealth generationfunction for its subordinates, which is then redistributed asappropriate or desired. The top level node will generally not maintainmore wealth than required, since this is inefficient, andredistributions incur their own inefficiencies.

The economy is preferably virtual, employing arbitrary value creditsgenerated using a cryptographic function. One possible exception iswhere external elements, such as cellular telephone carriers, areintegrated into the system. Since these are real economy agents, theremust be some interchange in value between credits and cash, unless thecellular carrier gains a benefit from the ad hoc network. One suchpossible benefit is extension of its fixed infrastructure to serveunder-covered areas. Another possible benefit is the ability to provideinformation from the ad hoc network to more remote areas. A furtherbenefit is the ability to use unlicensed spectrum for its activities ina standard and non-interfering manner.

In the virtual economy, each node has a physically and/or logicallysecure cryptographic module, which sequentially generates values whichhave a unique index number, and may be verified as to node and time oforigin, and possibly chain of owners. A node receiving this value cantherefore verify that it is authentic, its time of creation (andtherefore amortization schedule), and as an audit trail, the chain ofownership. Each bid is also cryptographically secure and signed, so thatif a node places a bid, and later fails to pay, a later investigationcan be conducted to correctly account for the transaction, and possiblypenalize wrongdoing. The payments for a communication are communicatedafter the transaction, in a cryptographic wrapper (cryptolope) destinedfor a target node. Since these are secure, the opportunity for theft islow, and there is little incentive for intentional delay of transmissionby any intermediate. Further, these packets may be transmitted alongredundant paths, to limit the ability of any one node to disruptcommunications.

The ability of a node to spend the same value packet twice is limited bya number of factors. First, since each node has a defined generatorfunction, if its spending exceeds its generation capacity, this will beapparent to nearby nodes. Second, since each packet has an index value,the other nodes may compare these values to make sure that they are notused more than once by any node, before they are transferred to anothernode. Since the value of the credit declines in value over time,indefinite period monitoring is not required.

In some instances, saving value may be an efficient strategy. In orderto take advantage of these gains, special bank nodes may be establishedwhich have the ability to hoard credits and then reissue new creditswhen required. Typically, there will be no interest, and in fact theremay be discount and delay. The net result of promoting savings willtypically be a reduction in demand with respect to supply, thusincreasing availability of resources. By allowing withdrawal of savings,periods of inflation and high peak demand is possible. Further, if thewithdrawn wealth has the same amortization schedule as newly generatedcredits, an event which provokes a “run on the bank” may result in arapid diminution of saved wealth, unless the immediate recipients bankthe newly transferred wealth.

As is seen, many of the economic institutions of the real economy haveequivalents in the virtual economy, and therefore may be employed intheir traditional and known roles to improve efficiency where theself-organizing features of the network alone incur correspondinginefficiencies, thus creating opportunities. Where necessary, links to areal economy, in order to pay for capital investment, efforts, orcompensate for risks, may be employed, however it is preferred thatthese links be attenuated in order to isolate the bulk of the ad hocnetwork from the influence of real-economy node wealth, and therefore topromote defection of those nodes who are disadvantaged thereby.

Second Embodiment

Multihop Ad Hoc Networks require cooperation of nodes which arerelatively disinterested in the content being conveyed. Typically, suchdisinterested intermediaries incur a cost for participation, forexample, power consumption or opportunity cost. Economic incentives maybe used to promote cooperation of disinterested intermediaries. Aneconomic optimization may be achieved using a market price-findingprocess, such as an auction. In many scenarios, the desire for thefairness of an auction is tempered by other concerns, i.e., there areconstraints on the optimization which influence price and parties of atransaction. For example, in military communication systems, rank may bedeemed an important factor in access to, and control over, thecommunications medium. A simple process of rank-based preemption,without regard for subjective or objective importance, will result in aninefficient economic distortion. In order to normalize the applicationof rank, one is presented with two options: imposing a normalizationscheme with respect to rank to create a unified economy, or consideringrank using a set of rules outside of the economy. One way to normalizerank, and the implicit hierarchy underlying the rank, is by treating theeconomy as an object-oriented hierarchy, in which each individualinherits or is allocated a subset of the rights of a parent, with peerswithin the hierarchy operating in a purely economic manner. Theextrinsic consideration of rank, outside of an economy, can bedenominated “respect”, which corresponds to the societal treatment ofthe issue, rather than normalizing this factor within the economy, inorder to avoid unintended secondary economic distortion. Each system hasits merits and limitations. An economic optimization is one involving atransaction in which all benefits and detriments can be expressed innormalized terms, and therefore by balancing all factors, includingsupply and demand, at a price, an optimum is achieved. Auctions are wellknown means to achieve an economic optimization between distinctinterests, to transfer a good or right in exchange for a market price.While there are different types of auctions, each having theirlimitations and attributes, as a class these are well accepted as ameans for transfer of goods or rights at an optimum price. Wheremultiple goods or rights are required in a sufficient combination toachieve a requirement, a so-called Vickrey-Clarke-Groves (VCG) auctionmay be employed. In such an auction, each supplier asserts a desiredprice for his component. The various combinations which meet therequirement are then compared, and the lowest cost combination selected.In a combinatorial supply auction, a plurality of buyers each seeks adivisible commodity, and each bids its best price. The bidders with thecombination of prices which are maximum are selected. In a commoditymarket, there are a plurality of buyers and sellers, so the auction ismore complex. In a market economy, the redistribution of goods orservices is typically transferred between those who value them least tothose who value them most. The transaction price depends on the balancebetween supply and demand; with the surplus being allocated to thelimiting factor.

There has thus been shown and described novel communications devices andsystems and methods which fulfill all the objects and advantages soughttherefore. Many changes, modifications, variations, combinations,subcombinations and other uses and applications of the subject inventionwill, however, become apparent to those skilled in the art afterconsidering this specification and the accompanying drawings whichdisclose the preferred embodiments thereof. All such changes,modifications, variations and other uses and applications which do notdepart from the spirit and scope of the invention are deemed to becovered by the invention, which is to be limited only by the claimswhich follow.

What is claimed is:
 1. An arbitrage agent, comprising: at least onememory storing system condition information representing an estimate ofa system condition; at least one memory storing accounting informationrepresenting an estimate of a system condition; an interface port to anautomated communication network; and at least one automated processor,configured to automatically engage in a transaction with an automatedremote agent, negotiated by communications through the interface port tothe automated communication network, wherein the transaction comprisesat least a communication of the system condition information, and acompensation for the communicated system condition information based onat least a portion of the economic surplus resulting from a decrease inuncertainty risk as a result of the communicated system conditioninformation, which results in a change in the stored accountinginformation.
 2. The arbitrage agent according to claim 1, wherein thesystem condition represents a state of the automated communicationnetwork.
 3. The arbitrage agent according to claim 1, wherein thecompensation comprises at least a portion of a value of the decrease inuncertainty risk over a transaction cost.
 4. The arbitrage agentaccording to claim 1, wherein the arbitrage agent is configured toreceive the system condition information through the interface port andto store the received system condition information in the at least onememory, to transmit accounting information to compensate for thereceived system condition information, and to produce at least oneoutput selectively dependent the stored system condition informationhaving an associated economic value and having an associated risk ofuncertainty that affects the associated economic value, wherein thereceived system condition information is adapted to reduce an estimateduncertainty risk and to increase an economic surplus associated with theat least one output.
 5. The arbitrage agent according to claim 1,wherein the system condition defines a condition of the automatedcommunication network, the arbitrage agent further comprises acommunication system model which estimates an optimum set ofcommunication parameters which are responsive to a communication systemtopology and risks associated with elements of the communication systemtopology, and the system condition information comprises informationwhich define at least one of a portion of a communication systemtopology and a risk associated with at least one element of thecommunication system topology, wherein the arbitrage agent negotiateswith the automated remote agent to receive relevant information toimprove its communication system model and thereby decrease itsuncertainty risk, wherein the at least one automated processor isfurther configured to conduct a communication distinct from thetransaction through the communication network dependent on thecommunication system model.
 6. The arbitrage agent according to claim 5,wherein the communications comprise packets of information, and areconducted through a packet router.
 7. The arbitrage agent according toclaim 1, wherein the arbitrage agent senses system conditioninformation, and communicates the sensed system condition information tothe automated remote agent as a result of the transaction.
 8. Thearbitrage agent according to claim 1, wherein the automatedcommunication network comprises a multihop communication network,wherein the uncertainty risk relates to an availability of at least onenode in the multihop communication network, and wherein the arbitrageagent is further configured to serve as a node of the multihop network.9. The arbitrage agent according to claim 1, wherein the communicationnetwork comprises a mobile ad hoc communication network, wherein theuncertainty risk relates to a function of at least one mobile node, andwherein the arbitrage agent is further physically associated with atleast one mobile node.
 10. The arbitrage agent according to claim 1,wherein the automated communication network changes dynamically, thearbitrage agent is configured to receive system condition informationfrom at least one automated remote agent in at least one firsttransaction for a first compensation, and to transmit system conditioninformation in at least one second transaction for a secondcompensation, and the at least one automated processor is configured tonegotiate the at least one first transaction and the at least one secondtransaction to product an economic profit based on an excess of thesecond compensation over the first compensation and a transactionalcost.
 11. The arbitrage agent according to claim 1, wherein the systemcondition information comprises a description of a respectiveuncertainty risk and an inclusion cost for members of a set of availableintermediary nodes in a multinode communication.
 12. The arbitrage agentaccording to claim 1, wherein the at least one automated processor isconfigured to negotiate the transaction with a participant in acommunication that has an associated risk tolerance, wherein thetransaction reduces the risk incurred by the participant and thearbitrage agent receives a payment of the compensation from theparticipant dependent on an amount of the reduction in risk.
 13. Thearbitrage agent according to claim 1, the arbitrage agent competes withother arbitrage agents for the transaction, and the compensation for thetransaction is competitively determined based on the competition.
 14. Anetworking system comprising: a network model, said model comprising aset of stored values representing estimates of a dynamically changingautomated communication network; a communication control, configured toautomatically control communications through the dynamically changingautomated communication network in dependence on said network model; andan arbitrage agent, comprising at least one automated processor,configured to engage in an automatic arbitrage transaction with respectto a risk that said set of stored values representing estimates of thedynamically changing communication network are incorrect.
 15. Thenetworking system according to claim 14, wherein the arbitrage agent isconfigured to automatically negotiate with external sources to receiveinformation adapted to improve the network model in consideration ofpayment of economic value, and to automatically negotiate with externalsinks to transmit information dependent on the network model inconsideration of receipt of economic value, wherein the arbitrage agentnegotiates to achieve an economic profit, based on at least payments ofeconomic value, the receipt of economic value, and a cost ofcommunications.
 16. The networking system according to claim 14, whereinthe network model comprises information describing a risk factor and aninclusion cost of a set of available intermediary nodes which engage inthe communications.
 17. The networking system according to claim 14,wherein the dynamically changing communication network comprises amobile ad hoc network.
 18. The networking system according to claim 14,wherein the arbitrage agent is configured to engage in the automaticarbitrage transaction with a participant in a communication that has anrisk tolerance with respect to the communication, wherein the arbitragetransaction reduces the risk incurred by the participant in thecommunication from a level above its risk tolerance to a level below itsrisk tolerance.
 19. The networking system according to claim 14, whereinthe communication control is configured to select one of a plurality offuture configurations of the dynamically changing communication network,wherein at least one of said future configurations has an associatedrisk of failure, and wherein the arbitrage agent automatically reduces apredicted risk of failure of the at least one future configuration inconsideration of an economic transaction.
 20. A method of routing acommunication, comprising: defining a source node, a destination node,and at least two intermediate nodes; estimating a network state of atleast one of the intermediate nodes; arbitraging a risk with respect toan accuracy of the estimate of network state with an automated arbitrageagent, wherein the automated arbitrage agent automatically receives aneconomic payment to at least one of reduce the risk and better definethe risk; and communicating between said source and said destination independence on the estimated network state, wherein the communication hasan economic value which is statistically increased by the at least oneof reduced risk and better defined risk, and the economic payment to theautomated arbitrage agent is less than the increase in the economicvalue of the communication.
 21. A method of optimizing an allocation ofresources within members of a community, comprising: (a) determiningsubjective resource value functions for a plurality of resources formembers of the community; (b) selecting an allocation of resources,optimized within an error limit to maximize an aggregate economicsurplus of the community; (c) charging members of the community inaccordance with the allocation of resources and respective subjectiveresource value functions of respective members receiving an allocation;and (d) distributing at least a portion of the economic surplus tomembers who defer receiving an allocation of resources as a result ofthe selecting.
 22. A method of optimizing a market, comprising: (a)providing at least four parties comprising at least one buyer, at leastone seller, and at least one deferring party with respect to atransaction; (b) matching bidders with offerors to maximize a surplus ofthe transaction; and (c) automatically determining the surplus andallocating a portion of the determined surplus to at least one of the atleast one deferring party using at least one automated processor, tomotivate deference.
 23. The method according to claim 22, wherein thetransaction comprises an auction transaction, the allocating beingadapted to encourage recruitment of entities into an auction,comprising: said matching comprising defining a set of prevailingparties from amongst the at least four parties as a result of receivingrespective bids in accordance with a set of auction rules; and saidautomatically determining comprising automatically computing adistribution a portion of the determined economic surplus to the atleast one of the at least one deferring party, in relation to amagnitude of a respective bid of a respective deferring party, whereinthe portion is computed to be less than an economic surplus to thedeferring party, to thereby incentivize the respective deferring partyto participate in the auction.
 23. The method according to claim 22,wherein the transaction is an automatically conducted combinatorialauction.